scholarly journals Identifying potential key genes and existing drugs for Multiple sclerosis, Schizophrenia, and Autism- an in silico approach

2021 ◽  
Author(s):  
SOUVIK CHAKRABORTY ◽  
Sajal Dey ◽  
Sushmita Bhowmick
Keyword(s):  
Multiple Sclerosis ◽  
Interaction Network ◽  
Gene Interaction ◽  
Autism Spectrum ◽  
Protein Protein Interaction ◽  
Interaction Database ◽  
Neurological Conditions ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Protein Protein Interaction Network

Nowadays, neurological conditions are a major concern as it not only preys on a patients health but also is a huge economic burden that is placed on the patients family. The diagnosis and treatment of disease sometimes cause methodological limitations. This is mainly common for individuals who have the signs of MS and schizophrenia (SZ). Patients suffering from multiple sclerosis are more likely to develop schizophrenia. Besides, a significant portion of patients who have been diagnosed with Autism Spectrum Disorder (ASD) later acquire the symptoms of Schizophrenia. In this study, we used bioinformatics tools to determine differentially expressed genes (DEGs) in all these diseases, and then we created a protein-protein interaction network using the online software STRING and identified 15 significant genes with the help of Cytohubba a plug-in tool in Cytoscape, the offline software (version3.8.2). We then used a drug-gene interaction database to conduct a drug-gene interaction study of the 15 hub genes and from there we showed that there are 37 existing FDA-approved drugs were obtained. These findings may provide a new and common therapeutic approach for MS, SZ, and ASD therapy.

scholarly journals Insights into the molecular mechanism of anticancer drug ruxolitinib repurposable in COVID-19 therapy

2021 ◽  
Author(s):  
Manisha Mandal ◽  
Shyamapada Mandal
Keyword(s):  
Biological Process ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Similar Response ◽  
Alveolar Cells ◽  
Protein Protein Interaction ◽  
Programming Tools ◽  
R Programming ◽  
Approved Drugs ◽  
Protein Protein Interaction Network

AbstractDue to non-availability of specific therapeutics against COVID-19, repurposing of approved drugs is a reasonable option. Cytokines imbalance in COVID-19 resembles cancer; exploration of anti-inflammatory agents, might reduce COVID-19 mortality. The current study investigates the effect of ruxolitinib treatment in SARS-CoV-2 infected alveolar cells compared to the uninfected one from the GSE5147507 dataset. The protein-protein interaction network, biological process and functional enrichment of differentially expressed genes were studied using STRING App of the Cytoscape software and R programming tools. The present study indicated that ruxolitinib treatment elicited similar response equivalent to that of SARS-CoV-2 uninfected situation by inducing defense response in host against virus infection by RLR and NOD like receptor pathways. Further, the effect of ruxolitinib in SARS-CoV-2 infection was mainly caused by significant suppression of IFIH1, IRF7 and MX1 genes as well as inhibition of DDX58/IFIH1-mediated induction of interferon-I and -II signalling.

scholarly journals Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

2020 ◽  
Author(s):  
Sugandh Kumar ◽  
Pratima Kumari ◽  
Geetanjali Agnihotri ◽  
Preethy VijayKumar ◽  
Shaheerah Khan ◽  
...  
Keyword(s):  
Interaction Network ◽  
Viral Proteins ◽  
Inhibitory Effect ◽  
Host Proteins ◽  
Disease Manifestation ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Approved Drugs ◽  
Viral Lifecycle ◽  
Fda Approved Drugs

<p>The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. </p> <p>In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect.</p> <p>Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.</p>

scholarly journals Identification of Drugs Targeting Multiple Viral and Human Proteins Using Computational Analysis for Repurposing Against COVID-19

2020 ◽  
Author(s):  
Sugandh Kumar ◽  
Pratima Kumari ◽  
Geetanjali Agnihotri ◽  
Preethy VijayKumar ◽  
Shaheerah Khan ◽  
...  
Keyword(s):  
Interaction Network ◽  
Viral Proteins ◽  
Inhibitory Effect ◽  
Host Proteins ◽  
Disease Manifestation ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Approved Drugs ◽  
Viral Lifecycle ◽  
Fda Approved Drugs

<p>The SARS-CoV2 is a highly contagious pathogen that causes a respiratory disease named COVID-19. The COVID-19 was declared a pandemic by the WHO on 11th March 2020. It has affected about 5.38 million people globally (identified cases as on 24th May 2020), with an average lethality of ~3%. Unfortunately, there is no standard cure for the disease, although some drugs are under clinical trial. Thus, there is an urgent need of drugs for the treatment of COVID-19. The molecularly targeted therapies have proven their utility in various diseases such as HIV, SARS, and HCV. Therefore, a lot of efforts are being directed towards the identification of molecules that can be helpful in the management of COVID-19. </p> <p>In the current studies, we have used state of the art bioinformatics techniques to screen the FDA approved drugs against thirteen SARS-CoV2 proteins in order to identify drugs for quick repurposing. The strategy was to identify potential drugs that can target multiple viral proteins simultaneously. Our strategy originates from the fact that individual viral proteins play specific role in multiple aspects of viral lifecycle such as attachment, entry, replication, morphogenesis and egress and targeting them simultaneously will have better inhibitory effect.</p> <p>Additionally, we analyzed if the identified molecules can also affect the host proteins whose expression is differentially modulated during SARS-CoV2 infection. The differentially expressed genes (DEGs) were identified using analysis of NCBI-GEO data (GEO-ID: GSE-147507). A pathway and protein-protein interaction network analysis of the identified DEGs led to the identification of network hubs that may play important roles in SARS-CoV2 infection. Therefore, targeting such genes may also be a beneficial strategy to curb disease manifestation. We have identified 29 molecules that can bind to various SARS-CoV2 and human host proteins. We hope that this study will help researchers in the identification and repurposing of multipotent drugs, simultaneously targeting the several viral and host proteins, for the treatment of COVID-19.</p>

Construction of a lncRNA–miRNA–mRNA network to determine the key regulators of the Th1/Th2 imbalance in multiple sclerosis

Epigenomics ◽  
2021 ◽  
Author(s):  
Hanieh Azari ◽  
Elham Karimi ◽  
Mohammad Shekari ◽  
Ahmad Tahmasebi ◽  
Amin Reza Nikpoor ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
T Helper ◽  
Protein Protein Interaction ◽  
Novel Biomarkers ◽  
Ifn Γ ◽  
Protein Protein Interaction Network ◽  
Competitive Endogenous Rna

Aim: The exact epigenetic mechanisms that determine the balance of T helper cells 1 and 2 (Th1/Th2) and autoimmune responses in multiple sclerosis (MS) remain unclear. We aim to clarify these. Methods: A combination of bioinformatics analysis and molecular evaluations was utilized to identify master hub genes. Results: A competitive endogenous RNA network containing six long noncoding RNAs (lncRNAs), 21 miRNAs and 86 mRNAs was provided through enrichment analysis and a protein–protein interaction network. NEAT1 and MALAT1 were found as differentially expressed lncRNAs using GEO (GSE21942). Quantitative real-time PCR results demonstrate dysregulation in the RUNX3 (a regulator of Th1/Th2 balance), GATA3 and TBX21, as well as miR-544a and miR-210-3p (which directly target RUNX3). ELISA also confirmed an imbalance in IFN-γ (Th1)/IL-4 (Th2) in MS patients. Conclusion: Our findings introduce novel biomarkers leading to Th1/Th2 imbalance in MS.

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

scholarly journals Diabetic Retinopathy and Laser Therapy in Rats: A Protein-Protein Interaction Network Analysis

2017 ◽  
Vol 8 (Suppl 1) ◽  
pp. S20-S21 ◽  
Author(s):  
Akram Safaei ◽  
Mostafa Rezaei Tavirani ◽  
Mona Zamanian Azodi ◽  
Alireza Lashay ◽  
Seyed Farzad Mohammadi ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Network Analysis ◽  
Laser Therapy ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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