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.

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 Getting into position: the catalytic mechanisms of protein ubiquitylation

2004 ◽  
Vol 379 (3) ◽  
pp. 513-525 ◽  
Author(s):  
Lori A. PASSMORE ◽  
David BARFORD
Keyword(s):  
Protein Interactions ◽  
Regulatory Elements ◽  
Substrate Selection ◽  
Protein Protein Interactions ◽  
Different Types ◽  
Catalytic Mechanisms ◽  
Cellular Pathways ◽  
Ubiquitin Conjugating Enzymes ◽  
Insight Into

The role of protein ubiquitylation in the control of diverse cellular pathways has recently gained widespread attention. Ubiquitylation not only directs the targeted destruction of tagged proteins by the 26 S proteasome, but it also modulates protein activities, protein–protein interactions and subcellular localization. An understanding of the components involved in protein ubiquitylation (E1s, E2s and E3s) is essential to understand how specificity and regulation are conferred upon these pathways. Much of what we know about the catalytic mechanisms of protein ubiquitylation comes from structural studies of the proteins involved in this process. Indeed, structures of ubiquitin-activating enzymes (E1s) and ubiquitin-conjugating enzymes (E2s) have provided insight into their mechanistic details. E3s (ubiquitin ligases) contain most of the substrate specificity and regulatory elements required for protein ubiquitylation. Although several E3 structures are available, the specific mechanistic role of E3s is still unclear. This review will discuss the different types of ubiquitin signals and how they are generated. Recent advances in the field of protein ubiquitylation will be examined, including the mechanisms of E1, E2 and E3. In particular, we discuss the complexity of molecular recognition required to impose selectivity on substrate selection and topology of poly-ubiquitin chains.

scholarly journals Epigenetic Mechanisms Involved in HCV-Induced Hepatocellular Carcinoma (HCC)

2021 ◽  
Vol 11 ◽  
Author(s):  
Pin Zhao ◽  
Samiullah Malik ◽  
Shaojun Xing
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Signaling Pathways ◽  
Hcv Infection ◽  
Specific Gene ◽  
Epigenetic Alterations ◽  
Chronic Hcv Infection ◽  
Mechanisms Of Carcinogenesis ◽  
Chronic Hcv ◽  
Insight Into

Hepatocellular carcinoma (HCC), is the third leading cause of cancer-related deaths, which is largely caused by virus infection. About 80% of the virus-infected people develop a chronic infection that eventually leads to liver cirrhosis and hepatocellular carcinoma (HCC). With approximately 71 million HCV chronic infected patients worldwide, they still have a high risk of HCC in the near future. However, the mechanisms of carcinogenesis in chronic HCV infection have not been still fully understood, which involve a complex epigenetic regulation and cellular signaling pathways. Here, we summarize 18 specific gene targets and different signaling pathways involved in recent findings. With these epigenetic alterations requiring histone modifications and DNA hyper or hypo-methylation of these specific genes, the dysregulation of gene expression is also associated with different signaling pathways for the HCV life cycle and HCC. These findings provide a novel insight into a correlation between HCV infection and HCC tumorigenesis, as well as potentially preventable approaches. Hepatitis C virus (HCV) infection largely causes hepatocellular carcinoma (HCC) worldwide with 3 to 4 million newly infected cases diagnosed each year. It is urgent to explore its underlying molecular mechanisms for therapeutic treatment and biomarker discovery. However, the mechanisms of carcinogenesis in chronic HCV infection have not been still fully understood, which involve a complex epigenetic regulation and cellular signaling pathways. Here, we summarize 18 specific gene targets and different signaling pathways involved in recent findings. With these epigenetic alterations requiring histone modifications and DNA hyper or hypo-methylation of these specific genes, the dysregulation of gene expression is also associated with different signaling pathways for the HCV life cycle and HCC. These findings provide a novel insight into a correlation between HCV infection and HCC tumorigenesis, as well as potentially preventable approaches.

scholarly journals Decreased Interactions between Calmodulin and a Mutant Huntingtin Model Might Reduce the Cytotoxic Level of Intracellular Ca2+: A Molecular Dynamics Study

2021 ◽  
Vol 22 (16) ◽  
pp. 9025
Author(s):  
Sanda Nastasia Moldovean ◽  
Vasile Chiş
Keyword(s):  
Molecular Dynamics ◽  
Protein Interactions ◽  
Structural Changes ◽  
Binding Free Energy ◽  
Point Mutations ◽  
Antagonistic Effect ◽  
Free Energy Calculations ◽  
Point Of View ◽  
Mutant Huntingtin ◽  
Protein Protein Interactions

Mutant huntingtin (m-HTT) proteins and calmodulin (CaM) co-localize in the cerebral cortex with significant effects on the intracellular calcium levels by altering the specific calcium-mediated signals. Furthermore, the mutant huntingtin proteins show great affinity for CaM that can lead to a further stabilization of the mutant huntingtin aggregates. In this context, the present study focuses on describing the interactions between CaM and two huntingtin mutants from a biophysical point of view, by using classical Molecular Dynamics techniques. The huntingtin models consist of a wild-type structure, one mutant with 45 glutamine residues and the second mutant with nine additional key-point mutations from glutamine residues into proline residues (9P(EM) model). Our docking scores and binding free energy calculations show higher binding affinities of all HTT models for the C-lobe end of the CaM protein. In terms of dynamic evolution, the 9P(EM) model triggered great structural changes into the CaM protein’s structure and shows the highest fluctuation rates due to its structural transitions at the helical level from α-helices to turns and random coils. Moreover, our proposed 9P(EM) model suggests much lower interaction energies when compared to the 45Qs-HTT mutant model, this finding being in good agreement with the 9P(EM)’s antagonistic effect hypothesis on highly toxic protein–protein interactions.

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.

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