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 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 New Drug Targets to Prevent Death due to Stroke: Results of Protein-Protein Interaction Network, Enrichment and Annotation Analyses.

2021 ◽  
Author(s):  
Michael Maes ◽  
Nikita Nikiforov ◽  
Kitiporn Plaimas ◽  
Apichat Suratanee ◽  
Edna Maria Reiche
Keyword(s):  
Protein Interaction ◽  
Drug Targets ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Fibrin Clot ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Type Plasminogen Activator ◽  
Urokinase Type Plasminogen Activator ◽  
Cell Surface Interactions

This study used established biomarkers of death due to ischemic stroke (IS) and performed network, enrichment, and annotation analysis. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF. Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4, TNF, JAK-STAT, NOD, IL10, IL13, IL4, and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell surface interactions, and the proteoglycan-integrin-extra cellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: a) that NFKB1, RELA and SP1 were the major regulating actors of the PPI network; and b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, VEGFA, proteoglycan-integrin-ECM complex, NFKB/RELA and SP1.

scholarly journals New Drug Targets to Prevent Death Due to Stroke: A Review Based on Results of Protein-Protein Interaction Network, Enrichment, and Annotation Analyses

2021 ◽  
Vol 22 (22) ◽  
pp. 12108
Author(s):  
Michael Maes ◽  
Nikita G. Nikiforov ◽  
Kitiporn Plaimas ◽  
Apichat Suratanee ◽  
Daniela Frizon Alfieri ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Drug Targets ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Fibrin Clot ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Type Plasminogen Activator ◽  
Urokinase Type Plasminogen Activator ◽  
Tgf Β1

This study used established biomarkers of death from ischemic stroke (IS) versus stroke survival to perform network, enrichment, and annotation analyses. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF. Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4, TNF, JAK-STAT, NOD, IL10, IL13, IL4, and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due to IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell-surface interactions, and the proteoglycan-integrin extracellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: (a) that NFKB1, RELA and SP1 were the major regulating actors of the PPI network; and (b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, the proteoglycan-integrin-ECM complex, TGF-β1/SMAD, NF-κB/RELA and SP1.

scholarly journals Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

2016 ◽  
Vol 113 (18) ◽  
pp. 4976-4981 ◽  
Author(s):  
Arunachalam Vinayagam ◽  
Travis E. Gibson ◽  
Ho-Joon Lee ◽  
Bahar Yilmazel ◽  
Charles Roesel ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Drug Targets ◽  
Interaction Network ◽  
Disease Genes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Disease States ◽  
Ppi Networks ◽  
Human Viruses ◽  
Potential Drug Targets

The protein–protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as “indispensable,” “neutral,” or “dispensable,” which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

scholarly journals Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Masoumeh Adhami ◽  
Balal Sadeghi ◽  
Ali Rezapour ◽  
Ali Akbar Haghdoost ◽  
Habib MotieGhader
Keyword(s):  
Network Analysis ◽  
Protein Interaction ◽  
Target Therapy ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Candidate Drug ◽  
Starting Point ◽  
Experimental Validations

Abstract Background The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules. Results Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19. Conclusions Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.

scholarly journals Protein–Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome

2019 ◽  
Vol 20 (12) ◽  
pp. 2959 ◽  
Author(s):  
Balqis Ramly ◽  
Nor Afiqah-Aleng ◽  
Zeti-Azura Mohamed-Hussein
Keyword(s):  
Network Analysis ◽  
Protein Interaction ◽  
Ribosome Biogenesis ◽  
Antigen Processing ◽  
Polycystic Ovarian Syndrome ◽  
Interaction Network ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ovarian Syndrome

Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein–protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher’s exact test and comorbidity data were used to identify PCOS–disease subnetworks. Pathway enrichment analysis was performed on the PCOS–disease subnetworks to identify significant pathways that are highly involved in the PCOS–disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.

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