scholarly journals In silico analysis for potential proteins and microRNAs in Glioblastoma and Parkinsonism.

2021 ◽  
Author(s):  
Sayak Banerjee ◽  
Souvik Chakraborty ◽  
Tarasankar Maiti ◽  
Sristi Biswas
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
In Silico Analysis ◽  
Gene Expression Omnibus ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Parkinsons Disease ◽  
Differential Expressed Genes ◽  
Microarray Datasets ◽  
Cellular Components

In todays world, neurodegenerative diseases such as Alzheimers disease, Parkinsons Disease, Huntingtons Disease as well as brain cancers such as astrocytomas, ependymomas, glioblastomas have become a great threat to us. In this study, we are trying to find a probable molecular connection associated with two very much different diseases, Glioblastoma, also known as Glioblastoma Multiforme (cancers of microglial cells of our brain) and Parkinsons disease. We at first downloaded the microarray datasets of these two diseases from Gene Expression Omnibus (GEO) and then analyzed them by the GEO2R tool. After analysis, we found 249 common upregulated differential expressed genes and 135 common downregulated differential expressed genes of these two diseases. Therefore the common differentially expressed genes, both upregulated and downregulated, were imported into STRING online tool to find out the protein-protein interactions. Now, this whole network was subjected to Cytoscape and the top ten hub genes were found by Cyto-Hubba plug-in. The top then hub genes are EGFR, CCNB1, CDK1, CCNA2, CHEK1, RAD51, MAD2L1, KIF20A, BUB1, and CCNB2. These all genes are upregulated in both diseases. To find out the biological processes, molecular functions, cellular components, and pathways associated with these hub genes Enrichr online software was used. We used miRNet software to determine the interactions of hub genes with microRNAs. This study will be useful in the future for drug targets discovery for these diseases.

scholarly journals Identification of hub genes and pathways in nonalcoholic steatohepatitis by integrated bioinformatics analysis

2021 ◽  
Author(s):  
Pegah Einaliyan ◽  
Ali Owfi ◽  
Mohammadamin Mahmanzar ◽  
Taha Aghajanzadeh ◽  
Morteza Hadizadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Principal Component ◽  
Gene Expression Omnibus ◽  
P Value ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Alcoholic Fatty Liver ◽  
Critical Pathways

AbstractBackgroundCurrently, non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases in the world. Forecasting the short-term, up to 2025, NASH due to fibrosis is one of the leading causes of liver transplantation. Cohort studies revealed that non-alcoholic steatohepatitis (NASH) has a higher risk of fibrosis progression among NAFLD patients. Identifying differentially expressed genes helps to determine NASH pathogenic pathways, make more accurate diagnoses, and prescribe appropriate treatment.Methods and ResultsIn this study, we found 11 NASH datasets by searching in the Gene Expression Omnibus (GEO) database. Subsequently, NASH datasets with low-quality control scores were excluded. Four datasets were analyzed with packages of R/Bioconductor. Then, all integrated genes were Imported into Cytoscape to illustrate the protein-protein interactions network. All hubs and nodes degree has been calculated to determine the hub genes with critical roles in networks.Possible correlations between expression profiles of mutual DEGs were identified employing Principal Component Analysis (PCA). Primary analyzed data were filtered based on gene expression (logFC > 1, logFC < −1) and adj-P-value (<0.05). Ultimately, among 379 DEGs, we selected the top 10 genes (MYC, JUN, EGR1, FOS, CCL2, IL1B, CXCL8, PTGS2, IL6, SERPINE1) as candidates among up and down regulated genes, and critical pathways such as IL-6, IL-17, TGF β, and TNFα were identified.ConclusionThe present study suggests an important DEGs, biological processes, and critical pathways involved in the pathogenesis of NASH disease. Further investigations are needed to clarify the exact mechanisms underlying the development and progression of NASH disease.

scholarly journals In-silico analysis: common biomarkers of NDs

2021 ◽  
Author(s):  
Deepanjan Sarkar ◽  
Souvik Chakraborty ◽  
Tarasankar Maiti ◽  
Sushmita Bhowmick
Keyword(s):  
Protein Interactions ◽  
Neurological Disorders ◽  
Biological Process ◽  
Kegg Pathway ◽  
In Silico Analysis ◽  
Molecular Function ◽  
Hub Genes ◽  
Parkinsons Disease ◽  
Online Tool ◽  
Silico Analysis

Neurodegenerative disorders (NDs) are a class of rapidly rising devastating diseases and the reason behind are might be an improper function of related genes or a mutation in a particular gene or even could be autoimmune also. Parkinsons disease (PD), Multiple sclerosis (MS), Huntingtons disease (HD) are some of the NDs, and still, incurable fully. Apart from the similarities in symptoms, there are common genes that express somehow a differential manner in patients of PDs, MSs, and HDs. A total of 1197 differentially expressed genes (DEGs) are obtained by analyzing the chosen datasets. The protein interactions by STRING online tool and degree sorted hubs obtained through a plug-in in Cytoscape; Cyto-Hubba. Among the sorted hubs KRAS, CREB1, PIK3CA, JAK2 are the ones that are not only common to all the studied datasets of NDs but also in other neurological disorders like Alzheimers. The enriched pathways with biological process, molecular function, cellular component, and KEGG pathway details are obtained and analyzed using Enricher. This paper frames that the obtained hub genes could be potential biomarkers also and a need for further drug design for finding a possible cure.

scholarly journals Identification of the hub genes RUNX2 and FN1 in gastric cancer

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 403-412
Author(s):  
Chao Han ◽  
Lei Jin ◽  
Xuemei Ma ◽  
Qin Hao ◽  
Huajun Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Protein Interactions ◽  
Lentiviral Vector ◽  
Gene Expression Omnibus ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Poorly Differentiated ◽  
Key Genes ◽  
Kaplan Meier Plotter

AbstractBackgroundThis study identified key genes in gastric cancer (GC) based on the mRNA microarray GSE19826 from the Gene Expression Omnibus (GEO) database and preliminarily explored the relationships among the key genes.MethodsDifferentially expressed genes (DEGs) were obtained using the GEO2R tool. The functions and pathway enrichment of the DEGs were analyzed using the Enrichr database. Protein–protein interactions (PPIs) were established by STRING. A lentiviral vector was constructed to silence RUNX2 expression in MGC-803 cells. The expression levels of RUNX2 and FN1 were measured. The influences of RUNX2 and FN1 on overall survival (OS) were determined using the Kaplan–Meier plotter online tool.ResultsIn total, 69 upregulated and 65 downregulated genes were identified. Based on the PPI network of the DEGs, 20 genes were considered hub genes. RUNX2 silencing significantly downregulated the FN1 expression in MGC-803 cells. High expression of RUNX2 and low expression of FN1 were associated with long survival time in diffuse, poorly differentiated, and lymph node-positive GC.ConclusionHigh RUNX2 and FN1 expression were associated with poor OS in patients with GC. RUNX2 can negatively regulate the secretion of FN1, and both genes may serve as promising targets for GC treatment.

scholarly journals Shared Blood Transcriptomic Signatures between Alzheimer’s Disease and Diabetes Mellitus

Biomedicines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 34
Author(s):  
Taesic Lee ◽  
Hyunju Lee
Keyword(s):  
Diabetes Mellitus ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interactions ◽  
Expression Patterns ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Gene Modules ◽  
Gene Regulatory ◽  
The Brain

Alzheimer’s disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.

scholarly journals Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

2017 ◽  
pp. 221-236 ◽  
Author(s):  
Alexander Goncearenco ◽  
Minghui Li ◽  
Franco L. Simonetti ◽  
Benjamin A. Shoemaker ◽  
Anna R. Panchenko
Keyword(s):  
Cancer Therapy ◽  
Protein Interactions ◽  
In Silico ◽  
Drug Targets ◽  
Protein Protein Interactions ◽  
Anti Cancer

scholarly journals Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bojun Xu ◽  
Lei Wang ◽  
Huakui Zhan ◽  
Liangbin Zhao ◽  
Yuehan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Gene Expression Omnibus ◽  
Complement Cascade ◽  
Hub Genes ◽  
Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

An integrated strategy for the discovery of drug targets by the analysis of protein–protein interactions

2004 ◽  
Vol 238 (2) ◽  
pp. 119-130 ◽  
Author(s):  
John M. Peltier ◽  
Srdjan Askovic ◽  
Robert R. Becklin ◽  
Cindy Lou Chepanoske ◽  
Yew-Seng J. Ho ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Protein Protein Interactions ◽  
Integrated Strategy

scholarly journals Protein Dynamics in F-like Bacterial Conjugation

Biomedicines ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 362
Author(s):  
Nicholas Bragagnolo ◽  
Christina Rodriguez ◽  
Naveed Samari-Kermani ◽  
Alice Fours ◽  
Mahboubeh Korouzhdehi ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Protein Interactions ◽  
Drug Targets ◽  
Dynamic Models ◽  
Structural Information ◽  
Imaging Techniques ◽  
Antibiotic Resistance Genes ◽  
Protein Protein Interactions ◽  
Secretion Systems ◽  
Type Iv

Efficient in silico development of novel antibiotics requires high-resolution, dynamic models of drug targets. As conjugation is considered the prominent contributor to the spread of antibiotic resistance genes, targeted drug design to disrupt vital components of conjugative systems has been proposed to lessen the proliferation of bacterial antibiotic resistance. Advancements in structural imaging techniques of large macromolecular complexes has accelerated the discovery of novel protein-protein interactions in bacterial type IV secretion systems (T4SS). The known structural information regarding the F-like T4SS components and complexes has been summarized in the following review, revealing a complex network of protein-protein interactions involving domains with varying degrees of disorder. Structural predictions were performed to provide insight on the dynamicity of proteins within the F plasmid conjugative system that lack structural information.

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