scholarly journals Analysis of Protein-Protein Interaction Network of Laminopathy Based on Topological Properties

2018 ◽  
Vol 11 (2) ◽  
pp. 1091-1103
Author(s):  
Sapana Singh Yadav ◽  
Usha Chouhan
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Drug Targets ◽  
Genetic Disorders ◽  
Seed Proteins ◽  
Interaction Network ◽  
Large Degree ◽  
Closeness Centrality ◽  
Ppi Network ◽  
Protein Protein Interaction

Laminopathy is a group of rare genetic disorders, including EDMD, HGPS, Leukodystrophy and Lipodystrophy, caused by mutations in genes, encoding proteins of the nuclear lamina. Analysis of protein interaction network in the cell can be the key to understand; how complex processes, lead to diseases. Protein-protein interaction (PPI) in network analysis provides the possibility to quantify the hub proteins in large networks as well as their interacting partners. A comprehensive genes/proteins dataset related to Laminopathy is created by analysing public proteomic data and text mining of scientific literature. From this dataset the associated PPI network is acquired to understand the relationships between topology and functionality of the PPI network. The extended network of seed proteins including one giant network consisted of 381 nodes connected via 1594 edges (Fusion) and 390 nodes connected via 1645 edges (Coexpression), targeted for analysis. 20 proteins with high BC and large degree have been identified. LMNB1 and LMNA with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from giant network with high BC proteins presents a clear and visual overview which shows all important proteins of Laminopathy and the crosstalk between them. Finally, the robustness of central proteins and accuracy of backbone are validated by 248 test networks. Based on the network topological parameters such as degree, closeness centrality, betweenness centrality we found out that integrated PPIN is centred on LMNB1 and LMNA. Although finding of other interacting partners strongly represented as novel drug targets for Laminopathy.

scholarly journals The Protein-Protein Interaction Network of First Episode Psychosis and Schizophrenia Reveals Possible Trigger Factors and New Drug Targets among Intracellular Signal Transduction Pathways and Neurotoxicity Processes

2021 ◽  
Author(s):  
Michael Maes ◽  
Kitiporn Plaimas ◽  
Apichat Suratanee ◽  
Cristiano Noto ◽  
Buranee Kanchanatawan
Keyword(s):  
Protein Interaction ◽  
Drug Targets ◽  
Interaction Network ◽  
Molecular Complexes ◽  
First Episode Psychosis ◽  
First Episode ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Episode Psychosis ◽  
Molecular Patterns

There is evidence that schizophrenia is characterized by activation of the immune-inflammatory response (IRS) and compensatory immune-regulatory (CIRS) systems and lowered neuroprotection. Studies performed on antipsychotic-naïve first episode psychosis (AF-FEP) and schizophrenia (FES) patients are important as they may disclose the pathogenesis of the disease. However, the interactome of FEP/FES is not well delineated. The aim of the current study was to delineate the characteristics of the protein-protein interaction (PPI) network of AN-FEP and its transition to FES and the biological functions, pathways, and molecular patterns, which are over-represented in FEP/FES. PPI network analysis shows that FEP and FEP/FES are strongly associated with a response to a bacterium, TNF, NFκB, RELA, SP1, JAK-STAT, death receptor and TLR4 signaling, and tyrosine phosphorylation of STAT proteins. Specific molecular complexes of the peripheral immune response are associated with microglial activation, neuroinflammation and gliogenesis. FEP/FES is accompanied by lowered protection against inflammation in part attributable to dysfunctional miRNA maturation, deficits in neurotrophin/Trk, RTK and Wnt/catenin signaling and adherens junction organization. Lowered neuroprotection due to reduced neurotrophin/Trk and Wnt/catenin signaling, and DISC1 expression and multiple interactions between lowered BDNF, CDH1, CTNNB, and DISC1 expression, increase the vulnerability to the neurotoxic effects of immune products including cytokines and complement factors. All pathways or molecular patterns enriched in the interactome of FEP/FES are directly or indirectly affected by LPS. In summary: FEP appears to be triggered by a biotic stimulus (e.g. Gram-negative bacteria) which may induce neuro-immune toxicity cascades especially when anti-inflammatory and neurotrophic protections are deficient.

scholarly journals Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data

2018 ◽  
Author(s):  
Juan Fernéndez-Tajes ◽  
Kyle J Gaulton ◽  
Martijn van de Bunt ◽  
Jason Torres ◽  
Matthias Thurner ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Insulin Secretion ◽  
Functional Connectivity ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Seed Proteins ◽  
Interaction Network ◽  
Biological Processes ◽  
Protein Protein Interaction

AbstractGenome wide association studies (GWAS) have identified several hundred susceptibility loci for Type 2 Diabetes (T2D). One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes. However, the causal variants identified by GWAS mostly fall into non-coding sequence, complicating the task of defining the effector transcripts through which they operate. Here, we describe implementation of an analytical pipeline to address this question. First, we integrate multiple sources of genetic, genomic, and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals. Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner Tree approach) which uses external, experimentally-confirmed protein-protein interaction (PPI) data to generate high confidence subnetworks. Third, we use GWAS data to test the T2D-association enrichment of the “non-seed” proteins introduced into the network, as a measure of the overall functional connectivity of the network. We find: (a) non-seed proteins in the T2D protein-interaction network so generated (comprising 705 nodes) are enriched for association to T2D (p=0.0014) but not control traits; (b) stronger T2D-enrichment for islets than other tissues when we use RNA expression data to generate tissue-specific PPI networks; and (c) enhanced enrichment (p=3.9×l0−5) when we combine analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion. These analyses reveal a pattern of non-random functional connectivity between causal candidate genes atT2D GWAS loci, and highlight the products of genes including YWHAG, SMAD4 or CDK2 as contributors to T2D-relevant islet dysfunction. The approach we describe can be applied to other complex genetic and genomic data sets, facilitating integration of diverse data types into disease-associated networks.Author summaryWe were interested in the following question: as we discover more and more genetic variants associated with a complex disease, such as type 2 diabetes, will the biological pathways implicated by those variants proliferate, or will the biology converge onto a more limited set of aetiological processes? To address this, we first took the 1895 genes that map to ~100 type 2 diabetes association signals, and pruned these to a set of 451 for which combined genetic, genomic and biological evidence assigned the strongest candidacy with respect to type 2 diabetes pathogenesis. We then sought to maximally connect these genes within a curated protein-protein interaction network. We found that proteins brought into the resulting diabetes interaction network were themselves enriched for diabetes association signals as compared to appropriate control proteins. Furthermore, when we used tissue-specific RNA abundance data to filter the generic protein-protein network, we found that the enrichment for type 2 diabetes association signals was enhanced within a network filtered for pancreatic islet expression, particularly when we selected the subset of diabetes association signals acting through reduced insulin secretion. Our data demonstrate convergence of the biological processes involved in type 2 diabetes pathogenesis and highlight novel contributors.

scholarly journals Application of Thermodynamics and Protein–Protein Interaction Network Topology for Discovery of Potential New Treatments for Temporal Lobe Epilepsy

2021 ◽  
Vol 11 (17) ◽  
pp. 8059
Author(s):  
Chang Yu ◽  
Edward A. Rietman ◽  
Hava T. Siegelmann ◽  
Marco Cavaglia ◽  
Jack A. Tuszynski
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Ribosomal Proteins ◽  
Drug Targets ◽  
Interaction Network ◽  
Current Standard ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

In this paper, we propose a bioinformatics-based method, which introduces thermodynamic measures and topological characteristics aimed to identify potential drug targets for pharmaco-resistant epileptic patients. We apply the Gibbs homology analysis to the protein–protein interaction network characteristic of temporal lobe epilepsy. With the identification of key proteins involved in the disease, particularly a number of ribosomal proteins, an assessment of their inhibitors is the next logical step. The results of our work offer a direction for future development of prospective therapeutic solutions for epilepsy patients, especially those who are not responding to the current standard of care.

Sign in / Sign up

Export Citation Format

Share Document