scholarly journals Systems Biomedicine of Rabies Delineates the Affected Signaling pathways

2016 ◽  
Author(s):  
Sadegh Azimzadeh Jamalkandi ◽  
Hamid Gholami Pourbadie ◽  
Mehdi Mirzaie ◽  
Sayed Hamid Reza Mozhgani ◽  
Farsheed Noorbakhsh ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Rabies Virus ◽  
Drug Targets ◽  
Psychological Symptoms ◽  
Interaction Network ◽  
Signal Propagation ◽  
Protein Protein Interaction ◽  
Infected Cells ◽  
Microarray Datasets ◽  
Whole Transcriptome

AbstractThe prototypical neurotropic virus, rabies, is a member of the Rhabdoviridae family that causes lethal encephalomyelitis. Although there have been a plethora of studies investigating the etiological mechanism of the rabies virus and many precautionary methods have been implemented to avert the disease outbreak over the last century, the disease has surprisingly no definite remedy at its late stages. The psychological symptoms and the underlying etiology, as well as the rare survival rate from rabies encephalitis, has still remained a mystery. We, therefore, undertook a systems biomedicine approach to identify the network of gene products implicated in rabies. This was done by meta-analyzing whole-transcriptome microarray datasets of the CNS infected by strain CVS-11, and integrating them with interactome data using computational and statistical methods. We first determined the differentially expressed genes (DEGs) in each study and horizontally integrated the results at the mRNA and microRNA levels separately. A total of 61 seed genes involved in signal propagation system were obtained by means of unifying mRNA and microRNA detected integrated DEGs. We then reconstructed a refined protein-protein interaction network (PPIN) of infected cells to elucidate biological signaling transduction pathways affected by rabies virus. To validate our findings, we confirmed differential expression of randomly selected genes in the network using Real-time PCR. In conclusion, the identification of seed genes and their network neighborhood within the refined PPIN can be useful for demonstrating signaling pathways including interferon circumvent, toward proliferation and survival, and neuropathological clue, explaining the intricate underlying molecular neuropathology of rabies infection and thus rendered a molecular framework for predicting potential drug targets.

scholarly journals A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

2020 ◽  
Vol 8 ◽  
Author(s):  
Ushashi Banerjee ◽  
Santhosh Sankar ◽  
Amit Singh ◽  
Nagasuma Chandra
Keyword(s):  
Drug Targets ◽  
Metabolic Flux ◽  
Latent Tuberculosis ◽  
Interaction Network ◽  
New Drugs ◽  
Prolonged Treatment ◽  
Flux Analysis ◽  
Protein Protein Interaction ◽  
Candidate Drug ◽  
Global Population

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

scholarly journals Identification of Potential Biomarkers and Pathways in Neonatal Hypoxic-Ischemic Brain Injury: Based on Bioinformatics Technology

2021 ◽  
Author(s):  
Shangbin Li ◽  
Shuangshuang Li ◽  
Qian Zhao ◽  
Jiayu Huang ◽  
Jinfeng Meng ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Gene Expression Omnibus ◽  
Biological Processes ◽  
Ischemic Brain ◽  
Protein Protein Interaction ◽  
Immune Mediated ◽  
Disability Rate ◽  
Potential Biomarker ◽  
Microarray Datasets ◽  
Cerebral Cortex Tissue

Abstract Background Neonatal hypoxic-ischemic brain damage (HIBD) is one of the most common serious diseases in newborns, with a high mortality and disability rate. This study aims to use the bioinformatics analysis to identify potential hematologic/immune systems tissue-specific genes and related signaling pathways neonatal HIBD.Methods Microarray datasets in HIBD were downloaded from the Gene Expression Omnibus database, and DEGs were identified by R software.Enrichment analyses were performed and protein–protein interaction networks were constructed to understand the functions and enriched pathways of DEGs and to identify central genes and key modules. Results In the cerebral cortex tissue with HIBD, 2598 DEGs were identified, including 2362 up-regulated and 236 down-regulated DEGs. In the blood with HIBD, 1442 DEGs were identified, including 540 up-regulated and 902 down-regulated DEGs. The results of biological processes and KEGG enrichment were very similar in DEGs of the two kinds of tissues, and both involved inflammation, immunity and apoptosis. The common DEGs of the two kinds of tissues also showed similar results in biological processes and KEGG enrichment.and four hematologic/immune system tissues specifically expressed potential biomarker genes were confirmed through a variety of methods, which were verified by GEO datasets and published experimental research. Conclusion The DEGs of HIBD including the potential peripheral biomarkers TYROBP, ITGAM, EGR1 and HMOX1, which may play a role in the pathogenesis of HIBD through inflammation and immune-mediated signaling pathways.

scholarly journals Potential Molecular Target Prediction and Docking Verification of Hua-Feng-Dan in Stroke Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ping Yang ◽  
Haifeng He ◽  
Shangfu Xu ◽  
Ping Liu ◽  
Xinyu Bai
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Target Genes ◽  
Target Prediction ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Core Network ◽  
Active Ingredients ◽  
Protein Protein Interaction ◽  
Highly Correlated

Objective. Hua-Feng-Dan (HFD) is a Chinese medicine for stroke. This study is to predict and verify potential molecular targets and pathways of HFD against stroke using network pharmacology. Methods. The TCMSP database and TCMID were used to search for the active ingredients of HFD, and GeneCards and DrugBank databases were used to search for stroke-related target genes to construct the “component-target-disease” by Cytoscape 3.7.1, which was further filtered by MCODE to build a core network. The STRING database was used to obtain interrelationships by topology and to construct a protein-protein interaction network. GO and KEGG were carried out through DAVID Bioinformatics. Autodock 4.2 was used for molecular docking. BaseSpace was used to correlate target genes with the GEO database. Results. Based on OB ≥ 30% and DL ≥ 0.18, 42 active ingredients were extracted from HFD, and 107 associated targets were obtained. PPI network and Cytoscape analysis identified 22 key targets. GO analysis suggested 51 cellular biological processes, and KEGG suggested that 60 pathways were related to the antistroke mechanism of HFD, with p53, PI3K-Akt, and apoptosis signaling pathways being most important for HFD effects. Molecular docking verified interactions between the core target (CASP8, CASP9, MDM2, CYCS, RELA, and CCND1) and the active ingredients (beta-sitosterol, luteolin, baicalein, and wogonin). The identified gene targets were highly correlated with the GEO biosets, and the stroke-protection effects of Xuesaitong in the database were verified by identified targets. Conclusion. HFD could regulate the symptoms of stroke through signaling pathways with core targets. This work provided a bioinformatic method to clarify the antistroke mechanism of HFD, and the identified core targets could be valuable to evaluate the antistroke effects of traditional Chinese medicines.

scholarly journals CSNK1A1 and Gli2 as Novel Targets Identified through an Integrative Analysis of Gene Expression Data, Protein–Protein Interaction and Pathways Networks in Glioblastoma Tumors: Can these Two be Antagonistic Proteins?

2014 ◽  
Vol 13 ◽  
pp. CIN.S18377 ◽  
Author(s):  
Seema Mishra
Keyword(s):  
Signaling Pathways ◽  
Drug Targets ◽  
Active Role ◽  
Integrative Approach ◽  
Significant Differential Expression ◽  
Shh Signaling ◽  
Protein Protein Interaction ◽  
Shh Pathway ◽  
Expression Of Genes ◽  
The Relationship

Glioblastoma (GBM) is the malignant form of glioma, and the interplay of different pathways working in concert in GBM development and progression needs to be fully understood. Wnt signaling and sonic hedgehog (SHH) signaling pathways, having basic similarities, are among the major pathways aberrantly activated in GBM, and hence, need to be targeted. It becomes imperative, therefore, to explore the functioning of these pathways in context of each other in GBM. An integrative approach may help provide new biological insights, as well as solve the problem of identifying common drug targets for simultaneous targeting of these pathways. The beauty of this approach is that it can recapitulate several known facts, as well as decipher new emerging patterns, identifying those targets that could be missed when relying on one type of data at a time. This approach can be easily extended to other systems to discover key patterns in the functioning of signaling molecules. Studies were designed to assess the relationship between significant differential expression of genes of the Wnt (Wnt/β-catenin canonical and Wnt non-canonical) and SHH signaling pathways and their connectivity patterns in interaction and signaling networks. Further, the aim was to decipher underlying mechanistic patterns that may be involved in a more specific way and to generate a ranked list of genes that can be used as markers or drug targets. These studies predict that Wnt pathway plays a relatively more pro-active role than the SHH pathway in GBM. Further, CTNNB1, CSNK1A1, and Gli2 proteins may act as key drug targets common to these pathways. While CTNNB1 is a widely studied molecule in the context of GBM, the likely roles of CSNK1A1 and Gli2 are found to be relatively novel. It is surmised that Gli2 may be antagonistic to CSNK1A1, preventing the phosphorylation of CTNNB1 and SMO proteins in Wnt and SHH signaling pathway, respectively, by CSNK1A1, and thereby, aberrant activation. New insights into the possible behavior of these pathway molecules relative to each other in GBM reveal some key interesting patterns.

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.

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