Identification of Novel SARS-CoV-2 Drug Targets by Host MicroRNAs and Transcription Factors Co-regulatory Interaction Network Analysis

Intervertebral disc (IVD) degeneration (IDD) is a multifactorial physiological process which is often associated with lower back pain. Previous studies have identified some molecular markers associated with disc degeneration, which despite their significant contributions, have provided limited insight into the etiology of IDD. In this study, we utilized a network medicine approach to uncover potential molecular mediators of IDD. Our systematic analyses of IDD associated with 284 genes included functional annotation clustering, interaction networks, network cluster analysis and Transcription factors (TFs)-target gene network analysis. The functional enrichment and protein–protein interaction network analysis highlighted the role of inflammatory genes and cytokine/chemokine signaling in IDD. Moreover, sub-network analysis identified significant clusters possessing organized networks of 24 cytokine and chemokine genes, which may be considered as key modulators for IDD. The expression of these genes was validated in independent microarray datasets. In addition, the regulatory network analysis identified the role of multiple transcription factors, with RUNX1 being a master regulator in the pathogenesis of IDD. Our analyses highlighted the role of cytokine genes and interacting pathways in IDD and further improved our understanding of the genetic mechanisms underlying IDD.

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Prediction of Leishmania Major Key Proteins via Topological Analysis of Protein-Protein Interaction Network

Galen Medical Journal ◽

10.31661/gmj.v7i0.1129 ◽

2018 ◽

Vol 7 ◽

pp. e1129

Author(s):

Nasrin Amiri Dashatan ◽

Mostafa Rezaie Tavirani ◽

Hakimeh Zali ◽

Mehdi Koushki ◽

Nayebali Ahmadi

Keyword(s):

Gene Ontology ◽

Network Analysis ◽

Protein Interaction Network ◽

Drug Targets ◽

Leishmania Major ◽

Interaction Network ◽

Expression Data ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network ◽

Hub Proteins

Background: Although leishmaniasis is regarded as a public health problem, no effective vaccine or decisive treatment has been introduced for this disease. Therefore, representing novel therapeutic proteins is essential. Protein-protein Interaction network analysis is a suitable tool to discover novel drug targets for leishmania major. To this aim, gene and protein expression data is used for instructing protein network and the key proteins are highlighted.Materials and Methods: In this computational and bioinformatics study, the protein/gene expression data related to leishmania major were studied, and 252 candidate proteins were extracted. Then, the protein networks of these proteins were explored and visualized by using String database and Cytoscape software. Finally, clustering and gene ontology were performed by MCODE and PANTHER databases, respectively.Results: Based on gene ontology analysis, most of the leishmania major proteins were located in cell compartments and membrane. Catalytic activity and binding were regarded as the relevant molecular functions and metabolic and cellular processes were the significant biological process. In this network analysis, UB-EP52, EF-2, chaperonin, Hsp70.4, Hsp60, tubulin alpha and beta chain, and ENOL and LACK were introduced as hub-bottleneck proteins. Based on clustering analysis, Lmjf.32.3270, ENOL and Lmjf.13.0290 were determined as seed proteins in each cluster.Conclusion: The results indicated that hub proteins play a significant role in pathogenesis and life cycle of leishmania major. Further studies of hubs will provide a better understanding of leishmaniasis mechanisms. Finally, these key hub proteins could be a suitable and helpful potential for drug targets and treating leishmaniasis by considering their validation. [GMJ.2018;7:e1129]

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Computational modeling and analysis of gene regulatory interaction network for metabolic disorder: a bioinformatics approach

Biointerface Research in Applied Chemistry ◽

10.33263/briac104.910917 ◽

2020 ◽

Vol 10 (4) ◽

pp. 5910-5917 ◽

Cited By ~ 4

Keyword(s):

Metabolic Disorder ◽

Drug Targets ◽

Regulatory Networks ◽

Interaction Network ◽

Ppi Network ◽

Regulatory Interaction ◽

Modeling And Analysis ◽

R Language ◽

Protein Protein Interaction ◽

Indirect Connection

In this study, we generate a PPI network and co-regulatory networks to understand the mechanisms of metabolic disorder more clearly. This study also analyzes the relevance of genes that are responsible for Cardiovascular (CVD), Obesity (OBS), Type 2 diabetes (T2D) and Hypertension (HT). It also showed the common genome among CVD, OBS, T2D, and HT. Using Bioinformatics approaches, drugs are possible to design. For this study gene was collected from NCBI (National Center for Biotechnology Information) using R language. Primarily, 7197 genes were found for CVD, 3140 are for OBS, 3283 genes were for T2D and 2237 are for HT which were responsible for all species. Among those genes, 12 top-weighted common genes were selected for this research. Using these liable common genes, a protein-protein interaction network (PPI) and a regulatory interaction network were constructed. The PPI network shows the interaction among those genes. And the regulatory interaction network defines the direct and indirect connection among selected genes. The PPI network will help to design more reliable drug targets.

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Identification of an interaction network of Arabidopsis transcription factors and MAP kinases involved in chitin signaling and pathogen defense

10.32469/10355/35382 ◽

2012 ◽

Author(s):

Geon Hui Son

Keyword(s):

Transcription Factors ◽

Map Kinases ◽

Interaction Network ◽

Pathogen Defense

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Trypanosome RNA Polymerases and Transcription Factors: Sensible Trypanocidal Drug Targets?

Current Drug Targets ◽

10.2174/138945008786786064 ◽

2008 ◽

Vol 9 (11) ◽

pp. 979-996 ◽

Cited By ~ 3

Author(s):

Luc Vanhamme

Keyword(s):

Transcription Factors ◽

Drug Targets ◽

Rna Polymerases

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Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

Current Gene Therapy ◽

10.2174/1566523219666190924113737 ◽

2019 ◽

Vol 19 (4) ◽

pp. 216-223 ◽

Cited By ~ 2

Author(s):

Tianyi Zhao ◽

Donghua Wang ◽

Yang Hu ◽

Ningyi Zhang ◽

Tianyi Zang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Drug Targets ◽

Molecular Mechanisms ◽

Feature Vector ◽

Mirna Gene ◽

Interaction Network ◽

Gene Interaction ◽

Proteinprotein Interaction ◽

Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

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In Silico Protein Interaction Network Analysis of Virulence Proteins Associated with Invasive Aspergillosis for Drug Discovery

Current Topics in Medicinal Chemistry ◽

10.2174/1568026619666181120150633 ◽

2019 ◽

Vol 19 (2) ◽

pp. 146-155 ◽

Cited By ~ 1

Author(s):

Renu Chaudhary ◽

Meenakshi Balhara ◽

Deepak Kumar Jangir ◽

Mehak Dangi ◽

Mrridula Dangi ◽

...

Keyword(s):

Network Analysis ◽

Invasive Aspergillosis ◽

Protein Interaction ◽

Drug Target ◽

Interaction Network ◽

Enrichment Analysis ◽

Homology Model ◽

Ppi Network ◽

Virulence Proteins ◽

Hub Proteins

<P>Background: Protein-Protein interaction (PPI) network analysis of virulence proteins of Aspergillus fumigatus is a prevailing strategy to understand the mechanism behind the virulence of A. fumigatus. The identification of major hub proteins and targeting the hub protein as a new antifungal drug target will help in treating the invasive aspergillosis. </P><P> Materials & Method: In the present study, the PPI network of 96 virulence (drug target) proteins of A. fumigatus were investigated which resulted in 103 nodes and 430 edges. Topological enrichment analysis of the PPI network was also carried out by using STRING database and Network analyzer a cytoscape plugin app. The key enriched KEGG pathway and protein domains were analyzed by STRING.Conclusion:Manual curation of PPI data identified three proteins (PyrABCN-43, AroM-34, and Glt1- 34) of A. fumigatus possessing the highest interacting partners. Top 10% hub proteins were also identified from the network using cytohubba on the basis of seven algorithms, i.e. betweenness, radiality, closeness, degree, bottleneck, MCC and EPC. Homology model and the active pocket of top three hub proteins were also predicted.</P>

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Transcriptome Analysis of Hypertrophic Heart Tissues from Murine Transverse Aortic Constriction and Human Aortic Stenosis Reveals Key Genes and Transcription Factors Involved in Cardiac Remodeling Induced by Mechanical Stress

Disease Markers ◽

10.1155/2019/5058313 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Peng Yu ◽

Baoli Zhang ◽

Ming Liu ◽

Ying Yu ◽

Ji Zhao ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Aortic Stenosis ◽

Mechanical Stress ◽

Cardiac Remodeling ◽

Interaction Network ◽

Cytokine Signaling ◽

Transverse Aortic Constriction ◽

Aortic Constriction ◽

Genomic Changes

Background. Mechanical stress-induced cardiac remodeling that results in heart failure is characterized by transcriptional reprogramming of gene expression. However, a systematic study of genomic changes involved in this process has not been performed to date. To investigate the genomic changes and underlying mechanism of cardiac remodeling, we collected and analyzed DNA microarray data for murine transverse aortic constriction (TAC) and human aortic stenosis (AS) from the Gene Expression Omnibus database and the European Bioinformatics Institute. Methods and Results. The differential expression genes (DEGs) across the datasets were merged. The Venn diagrams showed that the number of intersections for early and late cardiac remodeling was 74 and 16, respectively. Gene ontology and protein–protein interaction network analysis showed that metabolic changes, cell differentiation and growth, cell cycling, and collagen fibril organization accounted for a great portion of the DEGs in the TAC model, while in AS patients’ immune system signaling and cytokine signaling displayed the most significant changes. The intersections between the TAC model and AS patients were few. Nevertheless, the DEGs of the two species shared some common regulatory transcription factors (TFs), including SP1, CEBPB, PPARG, and NFKB1, when the heart was challenged by applied mechanical stress. Conclusions. This study unravels the complex transcriptome profiles of the heart tissues and highlighting the candidate genes involved in cardiac remodeling induced by mechanical stress may usher in a new era of precision diagnostics and treatment in patients with cardiac remodeling.

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