Protein-protein Interaction Network Visualization Research Articles

Differentially expressed extracellular matrix genes functionally separate ameloblastoma from odontogenic keratocyst

BackgroundAmeloblastoma and odontogenic keratocyst (OKC) are odontogenic tumors that develop from remnants of odontogenic epithelium. Both display locally invasive growth characteristics and high predilection for recurrence after surgical removal. Most ameloblastomas harbor BRAFV600E mutation while OKCs are associated with PATCH1 gene mutation but distinctive indicators of ameloblastoma growth characteristics relative to OKC are still unclear. The aim of this study was to assess hub genes that underlie ameloblastoma growth characteristics using bioinformatic analysis, ameloblastoma samples and mouse xenografts of human epithelial-derived ameloblastoma cells.MethodsRNA expression profiles were extracted from GSE186489 gene expression dataset acquired from Gene Expression Ominibus (GEO) database. Galaxy and iDEP online analysis tools were used to identify differentially expressed genes that were further characterized by gene ontology (GO) and pathway analysis using ShineyGO. The protein-protein interaction (PPI) network was constructed for significantly upregulated differentially expressed genes using online database STRING. The PPI network visualization was performed using Cytoscape and hub gene identification with cytoHubba. Top ten nodes were selected using maximum neighborhood component, degree and closeness algorithms and analysis of overlap was performed to confirm the hub genes. Epithelial-derived ameloblastoma cells from conventional ameloblastoma were transplanted into immunocompromised mice to recreate ameloblastoma in vivo based on the mouse xenograft model. The top 3 hub genes FN1, COL I and IGF-1 were validated by immunostaining and quantitative analysis of staining intensities to ameloblastoma, OKC samples and mouse ameloblastoma xenografts tissues.ResultsSeven hub genes were identified among which FN1, COL1A1/COL1A2 and IGF-1 are associated with extracellular matrix organization, collagen binding, cell adhesion and cell surface interaction. These were further validated by positive immunoreactivity within the stroma of ameloblastoma samples but both ameloblastoma xenograft and OKC displayed only FN1 and IGF-1 immunoreactivity while COL 1 was unreactive. The expression levels of both FN1 and IGF-1 were much lower in OKC relative to ameloblastoma.ConclusionThis study further validates a differentially upregulated expression of matrix proteins FN1, COL I and IGF-1 in ameloblastoma relative to OKC. It suggests that differential stromal architecture and growth characteristics of ameloblastoma relative to OKC could be an interplay of differentially upregulated genes in ameloblastoma.

Exploring the comorbidity mechanisms between psoriasis and obesity based on bioinformatics.

Psoriasis is a chronic, recurrent, immune-mediated inflammatory skin disease characterized by erythematous scaly lesions. Obesity is currently a major global health concern, increasing the risk of diseases such as cardiovascular diseases and diabetes. Since the correlation between psoriasis and obesity, as well as hypertension, diabetes, and cardiovascular diseases, has been clinically evidenced, it is of certain clinical significance to explore the mechanisms underlying the comorbidity of psoriasis with these conditions. Gene targets for both diseases were obtained from the Gene Expression Omnibus (GEO) comprehensive gene expression database. Differential gene analysis, intersection gene analysis, construction and visualization of protein-protein interaction networks (PPI) using R software, Cytoscape 3.8.2 software, online tools such as String, and enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed, with relevant graphics generated. Analysis identified 29 intersecting genes between the two diseases, with 10 key targets such as S100A7 and SERPINB4. Enrichment analysis indicated their involvement in regulating biological processes such as leukocyte chemotaxis, migration, and chronic inflammatory responses through cellular structures such as intracellular vesicles and extracellular matrix. Molecular functions, including RAGE receptor binding, Toll-like receptor binding, and fatty acid binding, were found to simultaneously regulate psoriasis and obesity. Psoriasis and obesity may mutually influence each other through multiple targets and pathways, emphasizing the importance of considering comorbidity treatment and daily care in clinical practice.

Active components and action mechanism of Shenmai Injection in treatment of atrial fibrillation based on network pharmacology and molecular docking

This study aims to explore the active components and molecular mechanism of Shenmai Injection in the treatment of atrial fibrillation(AF) based on the application of network pharmacology and molecular docking technology. The chemical components of single herbs of Shenmai Injection were collected from TCMSP and TCMID, with the standard chemical name and PubChem CID(referred to as CID) obtained from PubChem database. The active components were screened using SwissADME, and their targets were predicted using SwissTargetPrediction. Targets related to AF treatment were identified using GeneCards, OMIM, and other databases. Venn diagram was constructed using Venny 2.1 to obtain the intersection targets. The single herb-active component-potential target network was constructed using Cytoscape, and the clusterProfiler R function package was used to perform the gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG) pathway enrichment. The protein-protein interaction(PPI) network of intersection targets was generated based on the STRING database. The hub target protein was identified by visualization using Cytoscape, and then docked to its reverse-selected active components. The analysis showed that there were 65 active components with 681 corresponding targets in Shenmai Injection, 2 798 targets related to AF treatment, and 235 intersection targets involving 2 549 GO functions and 153 KEGG pathways. Finally, hub target proteins, including RAC-alpha serine/threonine-protein kinase(AKT1), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha(PIK3 CA), and estrogen receptor 1(ESR1), were screened out by PPI network visualization. The molecular docking was performed for 39 active components screened out in reverse, among which 30 active components de-monstrated high affinity. Among them, homoisoflavanoids CID 10871974, CID 5319742, and CID 10361149 had stronger affinity docking with AKT1. This study preliminarily indicates that Shenmai Injection treats AF through multiple components, multiple targets, and multiple pathways. Homoisoflavonoids of Ophiopogon japonicus are its important active components, which target AKT1 to regulate metabolism, inflammation, and apoptosis in AF treatment.

Analysis of sepsis-related genes through weighted gene co-expression network

To identify the Key genes in the development of sepsis through Weighted Gene Co-Expression Network Analysis (WGCNA). The gene expression dataset GSE154918 was downloaded from the public database Gene Expression Omnibus (GEO) database, which containes data from 105 microarrays of 40 control cases, 12 cases of asymptomatic infection, 39 cases of sepsis, and 14 cases of follow-up sepsis. The R software was used to screen out differentially expressed genes (DEG) in sepsis, and the Distributed Access View Integrated Database (DAVID), SEARCH TOOL FOR RETRIEVAL OF INTERACTING NEIGHBOURING GENES (STRING) and visualization software Cytoscape were used to perform gene function and pathway enrichment analysis, Protein-protein interaction (PPI) network analysis and key gene analysis to screen out the key genes in the development of sepsis. Forty-six candidate genes were obtained by WGCNA and combined with DEG expression analysis, and these 46 genes were analyzed by gene ontology (GO) and Kyoto City Encyclopedia of Genes and Genomes (KEGG) pathway enrichment to obtain gene functions and involved signaling pathways. The PPI network was further constructed using the STRING database, and 5 key genes were selected by the PPI network visualization software Cytoscape, including the mast cell expressed membrane protein 1 gene (MCEMP1), the S100 calcium-binding protein A12 gene (S100A12), the adipokine resistance factor gene (RETN), the c-type lectin structural domain family 4 member gene (CLEC4D), and peroxisome proliferator-activated receptor gene (PPARG), and differential expression analysis of each of these 5 genes showed that the expression levels of the above 5 genes were significantly upregulated in sepsis patients compared with healthy controls. In this study, 5 key genes related to sepsis were screened by constructing WGCNA method, which may be potential candidate targets related to sepsis diagnosis and treatment.

Automated Extraction and Visualization of Protein-Protein Interaction Networks and Beyond: A Text-Mining Protocol.

Proteins perform their functions by interacting with other proteins. Protein-protein interaction (PPI) is critical for understanding the functions of individual proteins, the mechanisms of biological processes, and the disease mechanisms. High-throughput experiments accumulated a huge number of PPIs in PubMed articles, and their extraction is possible only through automated approaches. The standard text-mining protocol includes four major tasks, namely, recognizing protein mentions, normalizing protein names and aliases to unique identifiers such as gene symbol, extracting PPIs, and visualizing the PPI network using Cytoscape or other visualization tools. Each task is challenging and has been revised over several years to improve the performance. We present a protocol based on our hybrid approaches and show the possibility of presenting each task as an independent web-based tool, NAGGNER for protein name recognition, ProNormz for protein name normalization, PPInterFinder for PPI extraction, and HPIminer for PPI network visualization. The protocol is specific to human but can be generalized to other organisms. We include KinderMiner, our most recent text-mining tool that predicts PPIs by retrieving significant co-occurring protein pairs. The algorithm is simple, easy to implement, and generalizable to other biological challenges.

Scalable Mining, Analysis, and Visualization of Protein-Protein Interaction Networks

Scalable Mining, Analysis, and Visualization of Protein-Protein Interaction Networks

Deep interactome profiling of membrane proteins by co-interacting protein identification technology.

Affinity purification coupled to mass spectrometry (AP-MS) is the method of choice for analyzing protein-protein interactions, but common protocols frequently recover only the most stable interactions and tend to result in low bait yield for membrane proteins. Here, we present a novel, deep interactome sequencing approach called CoPIT (co-interacting protein identification technology), which allows comprehensive identification and analysis of membrane protein interactomes and their dynamics. CoPIT integrates experimental and computational methods for a coimmunoprecipitation (Co-IP)-based workflow from sample preparation for mass spectrometric analysis to visualization of protein-protein interaction networks. The approach particularly improves the results for membrane protein interactomes, which have proven to be difficult to identify and analyze. CoPIT was used successfully to identify the interactome of the cystic fibrosis transmembrane conductance regulator (CFTR), demonstrating its validity and performance. The experimental step in this case achieved up to 100-fold-higher bait yield than previous methods by optimizing lysis, elution, sample clean-up and detection of interacting proteins by multidimensional protein identification technology (MudPIT). Here, we further provide evidence that CoPIT is applicable to other types of proteins as well, and that it can be successfully used as a general Co-IP method. The protocol describes all steps, ranging from considerations for experimental design, Co-IP, preparation of the sample for mass spectrometric analysis, and data analysis steps, to the final visualization of interaction networks. Although the experimental part can be performed in <3 d, data analysis may take up to a few weeks.

Comparison of Computational Tools for Protein-Protein Interaction (PPI) Mapping and Analysis

Several visualization tools for the mapping of protein-protein interactions have been developed in recent years. However, a systematic comparison of the virtues and limitations of different PPI visualization tools has not been carried out so far. In this study, we compare seven commonly used visualization tools, based on input and output file format, layout algorithm, database integration, Gene Ontology annotation and accessibility of each tool. The assessment was carried out based on brain disease datasets. Our suggested tools, NAViGaTOR, Cytoscape and Gephi perform competitively as PPI network visualization tools, can be a reference for future researches on PPI mapping and analysis.