In-silico analysis for RNA-interference mechanism of α-synuclein to treat Parkinson's disease

AbstractParkinson’s disease (PD) is a movement disorder caused by progressive degeneration of the midbrain dopaminergic (mDA) neurons in the substantia nigra pars compacta (SNc). Despite intense research efforts over the past decades, the etiology of PD remains largely unknown. Here, we discovered the involvement of trophoblast glycoprotein (Tpbg) in the development of PD-like phenotypes in mice. Tpbg expression was detected in the ventral midbrain during embryonic development and in mDA neurons in adulthood. Genetic ablation of Tpbg resulted in mild degeneration of mDA neurons in aged mice (12–14 months) with behavioral deficits reminiscent of PD symptoms. Through in silico analysis, we predicted potential TPBG-interacting partners whose functions were relevant to PD pathogenesis; this result was substantiated by transcriptomic analysis of the SNc of aged Tpbg knockout mice. These findings suggest that Tpbg is a new candidate gene associated with PD and provide a new insight into PD pathogenesis.

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In Silico Analysis of SNPs in PARK2 and PINK1 Genes That Potentially Cause Autosomal Recessive Parkinson Disease

Advances in Bioinformatics ◽

10.1155/2016/9313746 ◽

2016 ◽

Vol 2016 ◽

pp. 1-5 ◽

Cited By ~ 4

Author(s):

Yousuf Hasan Yousuf Bakhit ◽

Mohamed Osama Mirghani Ibrahim ◽

Mutaz Amin ◽

Yousra Abdelazim Mirghani ◽

Mohamed Ahmed Salih Hassan

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Structure Function ◽

Binding Site ◽

Ethnic Diversity ◽

In Silico ◽

Autosomal Recessive ◽

Neurodegenerative Disorder ◽

In Silico Analysis ◽

In Silico Prediction

Introduction. Parkinson’s disease (PD) is a common neurodegenerative disorder. Mutations in PINK1 are the second most common agents causing autosomal recessive, early onset PD. We aimed to identify the pathogenic SNPs in PARK2 and PINK1 using in silico prediction software and their effect on the structure, function, and regulation of the proteins. Materials and Methods. We carried out in silico prediction of structural effect of each SNP using different bioinformatics tools to predict substitution influence on protein structure and function. Result. Twenty-one SNPs in PARK2 gene were found to affect transcription factor binding activity. 185 SNPs were found to affect splicing. Ten SNPs were found to affect the miRNA binding site. Two SNPs rs55961220 and rs56092260 affected the structure, function, and stability of Parkin protein. In PINK1 gene only one SNP (rs7349186) was found to affect the structure, function, and stability of the PINK1 protein. Ten SNPs were found to affect the microRNA binding site. Conclusion. Better understanding of Parkinson’s disease caused by mutations in PARK2 and PINK1 genes was achieved using in silico prediction. Further studies should be conducted with a special consideration of the ethnic diversity of the different populations.

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Evaluation role of miR-124 in neurodegenerative diseases: literature review and in silico analysis

10.1101/2021.10.17.464692 ◽

2021 ◽

Author(s):

Javad Amini ◽

Bahram Bibak ◽

Amir R Afshar ◽

Amirhossein Sahebkar

Keyword(s):

Alzheimer’S Disease ◽

Parkinson’S Disease ◽

Literature Review ◽

Neurodegenerative Diseases ◽

Signaling Pathway ◽

In Silico ◽

In Silico Analysis ◽

Rna Seq ◽

Silico Analysis

Neurodegenerative diseases (ND) are characterized by loss of function and structure of neurons. NDs like Alzheimer's disease (AD) and Parkinson's disease (PD) have high burden on the society and patients. Currently microRNAs (miRNAs) approach is growing. miRNAs express in different tissues, especially in the central neuron systems (CNS). miRNAs have a dynamic role in the CNS among this miRNAs, miR-124 significantly express in the CNS. Studies on miR-124 have shown that miR-124 improves ND. In this study, we evaluated the role of miR-124 in the ND by literature review and in silico analysis. We used Pubmed database to find miR-124 function in the Alzheimer's disease, Parkinson's disease, Multiple sclerosis, Huntington's disease and amyotrophic lateral sclerosis. To better understand the role of miR-124 in the neurons, RNA-seq data form miR-124-deleted neuronal cells extracted from GEO database and analyzed in Galaxy platform. According literature review miR-124 attenuates inflammation and apoptosis in the ND by target NF-kb signaling pathway and regulation of BAX/BCL-2. miR-124 targets BACE1 and decreases level of Aβ. RNA-seq data showed miR-124 downregulation, an increase in chemokine gene like CCL1 and cytokine-cytokine receptor-interaction, as well as MAPK-signaling pathway. Our study shows that miR-124 can be promising therapeutic approaches to ND.

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A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

Seminars in Oncology ◽

10.1053/j.seminoncol.2020.02.012 ◽

2020 ◽

Vol 47 (6) ◽

pp. 398-408

Author(s):

Sonam Tulsyan ◽

Showket Hussain ◽

Balraj Mittal ◽

Sundeep Singh Saluja ◽

Pranay Tanwar ◽

...

Keyword(s):

Systematic Review ◽

Gallbladder Carcinoma ◽

In Silico ◽

In Silico Analysis ◽

Transcriptomic Profile ◽

Silico Analysis

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In Silico Analysis of Single Nucleotide Polymorphism in Human Prothrombin Gene

10.1055/s-0039-1680245 ◽

2019 ◽

Author(s):

I. Farah ◽

A. El-Mubark ◽

M. Osman ◽

A. Soliman ◽

F. Ali ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

In Silico ◽

In Silico Analysis ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Prothrombin Gene ◽

Silico Analysis

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Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

Current Medicinal Chemistry ◽

10.2174/0929867327666200727114410 ◽

2020 ◽

Vol 27 (38) ◽

pp. 6523-6535 ◽

Cited By ~ 3

Author(s):

Antreas Afantitis ◽

Andreas Tsoumanis ◽

Georgia Melagraki

Keyword(s):

Data Analysis ◽

Virtual Screening ◽

In Silico ◽

Model Development ◽

In Silico Analysis ◽

Material Design ◽

Efficient Solutions ◽

Biological Data ◽

Cost Efficient ◽

Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

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