scholarly journals Metabolomic network analysis of estrogen-stimulated MCF-7 cells: a comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis

2016 ◽  
Vol 91 (1) ◽  
pp. 217-230 ◽  
Author(s):  
Alexandra Maertens ◽  
Mounir Bouhifd ◽  
Liang Zhao ◽  
Shelly Odwin-DaCosta ◽  
Andre Kleensang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Pathway Analysis ◽  
Enrichment Analysis ◽  
Overrepresentation Analysis ◽  
Metabolite Network ◽  
Mcf 7

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

2019 ◽  
Vol 19 (2) ◽  
pp. 146-155 ◽  
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>

Determination of Usnic Acid Responsive miRNAs in Breast Cancer Cell Lines

2019 ◽  
Vol 19 (12) ◽  
pp. 1463-1472 ◽  
Author(s):  
Nil Kiliç ◽  
Yasemin Ö. Islakoğlu ◽  
İlker Büyük ◽  
Bala Gür-Dedeoğlu ◽  
Demet Cansaran-Duman
Keyword(s):  
Breast Cancer ◽  
Hedgehog Signaling ◽  
Treatment Options ◽  
Usnic Acid ◽  
Enrichment Analysis ◽  
Breast Cancer Cell Lines ◽  
Pathway Enrichment Analysis ◽  
Molecular Heterogeneity ◽  
Proliferative Effect ◽  
Mcf 7

Objective: Breast Cancer (BC) is the most common type of cancer diagnosed in women. A common treatment strategy for BC is still not available because of its molecular heterogeneity and resistance is developed in most of the patients through the course of treatment. Therefore, alternative medicine resources as being novel treatment options are needed to be used for the treatment of BC. Usnic Acid (UA) that is one of the secondary metabolites of lichens used for different purposes in the field of medicine and its anti-proliferative effect has been shown in certain cancer types, suggesting its potential use for the treatment. Methods: Anti-proliferative effect of UA in BC cells (MDA-MB-231, MCF-7, BT-474) was identified through MTT analysis. Microarray analysis was performed in cells treated with the effective concentration of UA and UA-responsive miRNAs were detected. Their targets and the pathways that they involve were determined using a miRNA target prediction tool. Results: Microarray experiments showed that 67 miRNAs were specifically responsive to UA in MDA-MB-231 cells while 15 and 8 were specific to BT-474 and MCF-7 cells, respectively. The miRNA targets were mostly found to play role in Hedgehog signaling pathway. TGF-Beta, MAPK and apoptosis pathways were also the prominent ones according to the miRNA enrichment analysis. Conclusion: The current study is important as being the first study in the literature which aimed to explore the UA related miRNAs, their targets and molecular pathways that may have roles in the BC. The results of pathway enrichment analysis and anti-proliferative effects of UA support the idea that UA might be used as a potential alternative therapeutic agent for BC treatment.

scholarly journals Signature RNAS and related regulatory roles in type 1 diabetes mellitus based on competing endogenous RNA regulatory network analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qinghong Shi ◽  
Hanxin Yao
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Network Analysis ◽  
Type 1 Diabetes Mellitus ◽  
Regulatory Network ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Competing Endogenous Rna ◽  
Pathway Enrichment

Abstract Background Our study aimed to investigate signature RNAs and their potential roles in type 1 diabetes mellitus (T1DM) using a competing endogenous RNA regulatory network analysis. Methods Expression profiles of GSE55100, deposited from peripheral blood mononuclear cells of 12 T1DM patients and 10 normal controls, were downloaded from the Gene Expression Omnibus to uncover differentially expressed long non-coding RNAs (lncRNAs), mRNAs, and microRNAs (miRNAs). The ceRNA regulatory network was constructed, then functional and pathway enrichment analysis was conducted. AT1DM-related ceRNA regulatory network was established based on the Human microRNA Disease Database to carry out pathway enrichment analysis. Meanwhile, the T1DM-related pathways were retrieved from the Comparative Toxicogenomics Database (CTD). Results In total, 847 mRNAs, 41 lncRNAs, and 38 miRNAs were significantly differentially expressed. The ceRNA regulatory network consisted of 12 lncRNAs, 10 miRNAs, and 24 mRNAs. Two miRNAs (hsa-miR-181a and hsa-miR-1275) were screened as T1DM-related miRNAs to build the T1DM-related ceRNA regulatory network, in which genes were considerably enriched in seven pathways. Moreover, three overlapping pathways, including the phosphatidylinositol signaling system (involving PIP4K2A, INPP4A, PIP4K2C, and CALM1); dopaminergic synapse (involving CALM1 and PPP2R5C); and the insulin signaling pathway (involving CBLB and CALM1) were revealed by comparing with T1DM-related pathways in the CTD, which involved four lncRNAs (LINC01278, TRG-AS1, MIAT, and GAS5-AS1). Conclusion The identified signature RNAs may serve as important regulators in the pathogenesis of T1DM.

scholarly journals Identification of key genes in calcific aortic valve disease via weighted gene co-expression network analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jin-Yu Sun ◽  
Yang Hua ◽  
Hui Shen ◽  
Qiang Qu ◽  
Jun-Yan Kan ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Network Analysis ◽  
Aortic Valve ◽  
Aortic Valve Disease ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Valve Disease ◽  
Hub Genes ◽  
Calcific Aortic Valve Disease ◽  
Underlying Mechanisms

Abstract Background Calcific aortic valve disease (CAVD) is the most common subclass of valve heart disease in the elderly population and a primary cause of aortic valve stenosis. However, the underlying mechanisms remain unclear. Methods The gene expression profiles of GSE83453, GSE51472, and GSE12644 were analyzed by ‘limma’ and ‘weighted gene co-expression network analysis (WGCNA)’ package in R to identify differentially expressed genes (DEGs) and key modules associated with CAVD, respectively. Then, enrichment analysis was performed based on Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, DisGeNET, and TRRUST database. Protein–protein interaction network was constructed using the overlapped genes of DEGs and key modules, and we identified the top 5 hub genes by mixed character calculation. Results We identified the blue and yellow modules as the key modules. Enrichment analysis showed that leukocyte migration, extracellular matrix, and extracellular matrix structural constituent were significantly enriched. SPP1, TNC, SCG2, FAM20A, and CD52 were identified as hub genes, and their expression levels in calcified or normal aortic valve samples were illustrated, respectively. Conclusions This study suggested that SPP1, TNC, SCG2, FAM20A, and CD52 might be hub genes associated with CAVD. Further studies are required to elucidate the underlying mechanisms and provide potential therapeutic targets.

Identification of Four Key Biomarkers and Small Molecule Drugs Innasopharyngeal Carcinoma by Weighted Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Xi Pan ◽  
Jian-Hao Liu
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Therapeutic Goals ◽  
Small Molecule Drugs

Abstract Background Nasopharyngeal carcinoma (NPC) is a heterogeneous carcinoma that the underlying molecular mechanisms involved in the tumor initiation, progression, and migration are largely unclear. The purpose of the present study was to identify key biomarkers and small-molecule drugs for NPC screening, diagnosis, and therapy via gene expression profile analysis. Methods Raw microarray data of NPC were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to screen out the potential differentially expressed genes (DEGs). The key modules associated with histology grade and tumor stage was identified by using weighted correlation network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the key module were performed to identify potential mechanisms. Candidate hub genes were obtained, which based on the criteria of module membership (MM) and high connectivity. Then we used receiver operating characteristic (ROC) curve to evaluate the diagnostic value of hub genes. The Connectivity map database was further used to screen out small-molecule drugs of hub genes. Results A total of 430 DEGs were identified based on two GEO datasets. The green gene module was considered as key module for the tumor stage of NPC via WGCNA analysis. The results of functional enrichment analysis revealed that genes in the green module were enriched in regulation of cell cycle, p53 signaling pathway, cell part morphogenesis. Furthermore, four DEGs-related hub genes in the green module were considered as the final hub genes. Then ROC revealed that the final four hub genes presented with high areas under the curve, suggesting these hub genes may be diagnostic biomarkers for NPC. Meanwhile, we screened out several small-molecule drugs that have provided potentially therapeutic goals for NPC. Conclusions Our research identified four potential prognostic biomarkers and several candidate small-molecule drugs for NPC, which may contribute to the new insights for NPC therapy.

MetENP/MetENPWeb: An R package and web application for metabolomics enrichment and pathway analysis in Metabolomics Workbench

2020 ◽  
Author(s):  
Kumari Sonal Choudhary ◽  
Eoin Fahy ◽  
Kevin Coakley ◽  
Manish Sud ◽  
Mano R Maurya ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Web Application ◽  
Enrichment Analysis ◽  
R Package ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Kegg Pathways ◽  
Link Type ◽  
Species Specific ◽  
User Friendly

ABSTRACTWith the advent of high throughput mass spectrometric methods, metabolomics has emerged as an essential area of research in biomedicine with the potential to provide deep biological insights into normal and diseased functions in physiology. However, to achieve the potential offered by metabolomics measures, there is a need for biologist-friendly integrative analysis tools that can transform data into mechanisms that relate to phenotypes. Here, we describe MetENP, an R package, and a user-friendly web application deployed at the Metabolomics Workbench site extending the metabolomics enrichment analysis to include species-specific pathway analysis, pathway enrichment scores, gene-enzyme information, and enzymatic activities of the significantly altered metabolites. MetENP provides a highly customizable workflow through various user-specified options and includes support for all metabolite species with available KEGG pathways. MetENPweb is a web application for calculating metabolite and pathway enrichment analysis.Availability and ImplementationThe MetENP package is freely available from Metabolomics Workbench GitHub: (https://github.com/metabolomicsworkbench/MetENP), the web application, is freely available at (https://www.metabolomicsworkbench.org/data/analyze.php)

scholarly journals Hepatic fat is a stronger correlate of key clinical and molecular abnormalities than visceral and abdominal subcutaneous fat in youth

2020 ◽  
Vol 8 (1) ◽  
pp. e001126
Author(s):  
Catherine E Cioffi ◽  
K M Venkat Narayan ◽  
Ken Liu ◽  
Karan Uppal ◽  
Dean P Jones ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Network Analysis ◽  
Subcutaneous Fat ◽  
Enrichment Analysis ◽  
Fat Distribution ◽  
Pathway Enrichment Analysis ◽  
Clinical Variables ◽  
Pathway Enrichment ◽  
Hepatic Fat ◽  
Integrative Network

IntroductionBody fat distribution is strongly associated with cardiometabolic disease (CMD), but the relative importance of hepatic fat as an underlying driver remains unclear. Here, we applied a systems biology approach to compare the clinical and molecular subnetworks that correlate with hepatic fat, visceral fat, and abdominal subcutaneous fat distribution.Research design and methodsThis was a cross-sectional sub-study of 283 children/adolescents (7–19 years) from the Yale Pediatric NAFLD Cohort. Untargeted, high-resolution metabolomics (HRM) was performed on plasma and combined with existing clinical variables including hepatic and abdominal fat measured by MRI. Integrative network analysis was coupled with pathway enrichment analysis and multivariable linear regression (MLR) to examine which metabolites and clinical variables associated with each fat depot.ResultsThe data divided into four communities of correlated variables (|r|>0.15, p<0.05) after integrative network analysis. In the largest community, hepatic fat was associated with eight clinical biomarkers, including measures of insulin resistance and dyslipidemia, and 878 metabolite features that were enriched predominantly in amino acid (AA) and lipid pathways in pathway enrichment analysis (p<0.05). Key metabolites associated with hepatic fat included branched-chain AAs (valine and isoleucine/leucine), aromatic AAs (tyrosine and tryptophan), serine, glycine, alanine, and pyruvate, as well as several acylcarnitines and glycerophospholipids (all q<0.05 in MLR adjusted for covariates). The other communities detected in integrative network analysis consisted of abdominal visceral, superficial subcutaneous, and deep subcutaneous fats, but no clinical variables, fewer metabolite features (280, 312, and 74, respectively), and limited findings in pathway analysis.ConclusionsThese data-driven findings show a stronger association of hepatic fat with key CMD risk factors compared with abdominal fats. The molecular network identified using HRM that associated with hepatic fat provides insight into potential mechanisms underlying the hepatic fat–insulin resistance interface in youth.

scholarly journals Deep Sequencing and Analysis of Transcriptomes of Pinus koraiensis Sieb. & Zucc.

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 350
Author(s):  
Shaolin Shi ◽  
Siyu Yan ◽  
Chao Zhao ◽  
Peng Zhang ◽  
Ling Yang ◽  
...  
Keyword(s):  
Abscisic Acid ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Deep Sequencing ◽  
Transcriptome Sequencing ◽  
Enrichment Analysis ◽  
Reproductive Development ◽  
Pinus Koraiensis ◽  
Apical Buds ◽  
Low Levels

The objective of this research was to study the differences in endogenous hormone levels and the genes related to reproductive development in Chinese pinenut (Pinus koraiensis) trees of different ages. The apical buds of P. koraiensis were collected from 2-, 5-, 10-, 15-, and 30-year-old plants and also from grafted plants. There were three replicates from each group used for transcriptome sequencing. After assembly and annotation, we identified the differentially expressed genes (DEGs) and performed enrichment analysis, pathway analysis, and expression analysis of the DEGs in each sample. The results showed that unigenes related to reproductive development, such as c64070.graph_c0 and c68641.graph_c0, were expressed at relatively low levels at young ages, and that the relative expression gradually increased with increasing plant age. In addition the highest expression levels were reached around 10 and 15 years of age, after which they gradually decreased. Moreover, some unigenes, such as c61855.graph_c0, were annotated as abscisic acid hydroxylase genes, and the expression of c61855.graph_c0 gradually declined with increasing age in P. koraiensis.

scholarly journals Differential co-expression network analysis for identification of functional modules in HIV infection and tubercolosis-HIV co-infection

2016 ◽  
Vol 89 (2) ◽  
Author(s):  
Mohit Jha ◽  
Anvita Gupta ◽  
Sudha Singh ◽  
Khushhali Menaria Pandey
Keyword(s):  
Network Analysis ◽  
Hiv Infection ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Gene Clusters ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Specific Gene ◽  
Diagnostic Strategies

Co-infection with tuberculosis (TB) is the preeminent cause of demise in human immunodeficiency virus (HIV) infected individuals. However, diagnosis of TB, particularly in the presence of an HIV co-infection, can be limiting owing to the high inaccuracy associated with conventional diagnostic strategies. Here we determine dysregulated pathways in TB-HIV co-infection and HIV infection utilizing coexpression networks. Primarily, we utilized preservation statistics to identify gene modules that exhibit a weak conservation of network topology within HIV infected and TB-HIV co-infected networks. Raw data was downloaded from Gene Expression Omnibus (GSE50834) and duly pre-processed. Co-expression networks for each condition (HIV infected and TB-HIV co-infected) were constructed independently. Preservation of HIV infected network edges was evaluated with respect to TB-HIV co-infected and vice versa using weighted correlation network analysis. Two out of the 22 modules were identified as exhibiting weak preservation in both conditions. Functional enrichment analysis identified that weakly preserved modules were pertinent to the condition under study. For instance, weakly preserved TBHIV co-infected module T1 enriched for genes associated with mitochondrion exhibited the highest fraction of gene interaction pairs exclusive to TB-HIV co-infection. Concisely, we illustrated the application of using preservation statistics to detect modules functionally linked with dysregulated pathways in disease, as exemplified by the mitochondrion module T1. Our analyses discovered gene clusters that are non-randomly linked with the disease. Highly specific gene pairs pointed to interactions between known markers of disease and favoured identification of possible markers that are likely to be associated with the disease.

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