scholarly journals Monozygotic Twins Discordant for Immunoglobulin A Nephropathy Display Differences in DNA Methylation and Gene Expression

2020 ◽  
pp. 1-10
Author(s):  
Min Wei ◽  
Sijun Meng ◽  
Sufang Shi ◽  
Lijun Liu ◽  
Xujie Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Expression Profiles ◽  
Immunoglobulin A ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Genome Wide ◽  
Mz Twins

<b><i>Introduction:</i></b> Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis. It involves both genetic and environmental factors, among which DNA methylation, the most studied epigenetic modification, was shown to play a role. Here, we assessed genome-wide DNA methylation and gene expression profiles in 2 pairs of IgAN-discordant monozygotic (MZ) twins, in order to characterize methylation changes and their potential influences on gene expression in IgAN. <b><i>Methods:</i></b> Genome-wide DNA methylation and gene expression profiles were evaluated in peripheral blood mononuclear cells obtained from 2 IgAN-discordant MZ twins. Differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were detected, and an integrated analysis was performed. Finally, functional enrichment analysis was done for DMR-associated genes and DEGs. <b><i>Results:</i></b> Totally 521 DMRs were detected for 2 IgAN-discordant MZ twins. Among them, 9 DMRs were found to be mapped to genes that differentially expressed in 2 MZ twins, indicating the potential regulatory mechanisms of expression for these 9 genes (<i>MNDA</i>, <i>DYSF</i>, <i>IL1R2</i>, <i>TLR6</i>, <i>TREML2</i>, <i>TREM1</i>, <i>IL32</i>, <i>S1PR5</i>, and <i>ADGRE3</i>) in IgAN. Biological process analysis of them showed that they were mostly involved in the immune system process. Functional enrichment analysis of DEGs and DMR-associated genes both identified multiple pathways relevant to inflammatory and immune responses. And DMR-associated genes were significantly enriched in terms related to T-cell function. <b><i>Conclusions:</i></b> Our findings indicate that changes in DNA methylation patterns were involved in the pathogenesis of IgAN. Nine target genes detected in our study may provide new ideas for the exploration of molecular mechanisms of IgAN.

scholarly journals Understanding the Modus Operandi of MicroRNA Regulatory Clusters

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1103 ◽  
Author(s):  
Arthur C. Oliveira ◽  
Luiz A. Bovolenta ◽  
Lucas Alves ◽  
Lucas Figueiredo ◽  
Amanda O. Ribeiro ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Target Prediction ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Modus Operandi ◽  
Wide Range

MicroRNAs (miRNAs) are non-coding RNAs that regulate a wide range of biological pathways by post-transcriptionally modulating gene expression levels. Given that even a single miRNA may simultaneously control several genes enrolled in multiple biological functions, one would expect that these tiny RNAs have the ability to properly sort among distinctive cellular processes to drive protein production. To test this hypothesis, we scrutinized previously published microarray datasets and clustered protein-coding gene expression profiles according to the intensity of fold-change levels caused by the exogenous transfection of 10 miRNAs (miR-1, miR-7, miR-9, miR-124, miR-128a, miR-132, miR-133a, miR-142, miR-148b, miR-181a) in a human cell line. Through an in silico functional enrichment analysis, we discovered non-randomic regulatory patterns, proper of each cluster identified. We demonstrated that miRNAs are capable of equivalently modulate the expression signatures of target genes in regulatory clusters according to the biological function they are assigned to. Moreover, target prediction analysis applied to ten vertebrate species, suggest that such miRNA regulatory modus operandi is evolutionarily conserved within vertebrates. Overall, we discovered a complex regulatory cluster-module strategy driven by miRNAs, which relies on the controlled intensity of the repression over distinct targets under specific biological contexts. Our discovery helps to clarify the mechanisms underlying the functional activity of miRNAs and makes it easier to take the fastest and most accurate path in the search for the functions of miRNAs in any distinct biological process of interest.

scholarly journals Identification of Key Candidate Biomarkers and Pathways in Atherosclerosis

2020 ◽  
Author(s):  
Zhijun Meng ◽  
Jia Gao ◽  
Hongping Liang ◽  
Caihong Liu ◽  
Jianli Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Effective Prevention ◽  
Diagnostic Indicator

Abstract Background Atherosclerosis is the leading cause of cardiovascular disease worldwide for which lacks effective prevention and therapeutic strategy. Therefore, clinical indicators for early diagnosis and screening are in great need. The present study aimed to elucidate the key genetic signatures and pathways identifying the key candidate biomarker in atherosclerosis by integrative bioinformatics analysis combining with experimental assay. Methods The gene expression profiles (GSE30169, GSE6584) were achieved from the Gene Expression Omnibus database. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to examine the biological functions of identified differentially expressed genes (DEGs). A protein-protein interaction (PPI) network was mapped using Cytoscape software. Results 91 DEGs were identified, including 68 up-regulated genes and 23 down-regulated genes. Functional enrichment analysis indicated that DEGs genes were significantly enriched in ferroptosis, TNF signaling pathway, IL-17 signaling pathway. 12 nodes with the highest degrees were selected as hub genes. CCL2, CXCL1, IL6, and DUSP1 can serve as a sensitive diagnostic indicator for the early stage of atherosclerosis; CEBPB and HMOX1 can serve as a diagnostic indicator for diabetic atherosclerosis; TRIB3 is a sensitive marker indicating atherosclerosis risks in diabetic women group.Conclusions In conclusion, we have identified key candidate genes that indicate the diagnosis of patients with atherosclerosis, and these genes may serve as potential therapeutic or drug development targets for atherosclerosis.

Identify Key Genes by Weighted Gene Co-Expression Network Analysis for Lung Adenocarcinoma

Nano LIFE ◽  
2019 ◽  
Vol 09 (01n02) ◽  
pp. 1940002
Author(s):  
Jichen Xu ◽  
Xianchun Zong ◽  
Qianshu Ren ◽  
Hongyu Wang ◽  
Lijuan Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Oocyte Meiosis ◽  
Key Genes

The aim of this paper is to identify key genes in lung adenocarcinoma (LUAD) through weighted gene co-expression network analysis (WGCNA), and to further understand the molecular mechanism of LUAD. 107 gene expression profiles were downloaded from GSE10072 in the GEO database. We performed rigorous processing of the initial gene expression profile data. Subsequently, we used WGCNA to identify disease-driven modules and enforced functional enrichment analysis. The key genes were defined as the most connected genes in the driver module and were validated using the GSE75037 and TCGA database. GSE10072 removed 41 unpaired lung samples and 4 outliers. By analyzing the 62 samples using WGCNA, we obtained 26 modules and identified the brown and magenta modules as the driving modules for the LUAD. We found that the “Cell cycle”, “Oocyte meiosis” and “Progesterone-mediated oocyte maturation” pathways may be related to the occurrence of LUAD. GSE75037 removed 8 outlier and obtained 2909 differentially expressed genes (DEGs), 26 genes (9 genes in the brown module, 17 genes in the magenta module) overlap with key genes in the driver module. The results of the survival analysis suggest that 19 genes were significantly correlated with the patient’s survival time, including KPNA2, FEN1, RRM2, TOP2A, CENPF, MCM4, BIRC5, MELK, MAD2L1, CCNB1, CCNA2, KIF11, CDKN3, NUSAP1, CEP55, AURKA, NEK2, KIF14 and CDCA8, which may be potential biomarkers or therapeutic targets for LUAD. In this study, we provide a theoretical basis for further understanding the biological mechanism of LUAD through bioinformatics analysis of LUAD.

scholarly journals reString: an open-source Python software to perform automatic functional enrichment retrieval, results aggregation and data visualization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stefano Manzini ◽  
Marco Busnelli ◽  
Alice Colombo ◽  
Elsa Franchi ◽  
Pasquale Grossano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Statistical Tests ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Knowledge Bases ◽  
Functional Enrichment ◽  
Reference Database ◽  
Multiple User ◽  
Daunting Task

AbstractFunctional enrichment analysis is an analytical method to extract biological insights from gene expression data, popularized by the ever-growing application of high-throughput techniques. Typically, expression profiles are generated for hundreds to thousands of genes/proteins from samples belonging to two experimental groups, and after ad-hoc statistical tests, researchers are left with lists of statistically significant entities, possibly lacking any unifying biological theme. Functional enrichment tackles the problem of putting overall gene expression changes into a broader biological context, based on pre-existing knowledge bases of reference: database collections of known expression regulation, relationships and molecular interactions. STRING is among the most popular tools, providing both protein–protein interaction networks and functional enrichment analysis for any given set of identifiers. For complex experimental designs, manually retrieving, interpreting, analyzing and abridging functional enrichment results is a daunting task, usually performed by hand by the average wet-biology researcher. We have developed reString, a cross-platform software that seamlessly retrieves from STRING functional enrichments from multiple user-supplied gene sets, with just a few clicks, without any need for specific bioinformatics skills. Further, it aggregates all findings into human-readable table summaries, with built-in features to easily produce user-customizable publication-grade clustermaps and bubble plots. Herein, we outline a complete reString protocol, showcasing its features on a real use-case.

scholarly journals Integrated DNA Methylation and Gene Expression Analysis Identified S100A8 and S100A9 in the Pathogenesis of Obesity

2021 ◽  
Vol 8 ◽  
Author(s):  
Ningyuan Chen ◽  
Liu Miao ◽  
Wei Lin ◽  
Donghua Zou ◽  
Ling Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Mrna Expression ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Functional Enrichment Analysis ◽  
Normal Subjects ◽  
Functional Enrichment ◽  
Differentially Methylated Genes

Background: To explore the association of DNA methylation and gene expression in the pathology of obesity.Methods: (1) Genomic DNA methylation and mRNA expression profile of visceral adipose tissue (VAT) were performed in a comprehensive database of gene expression in obese and normal subjects. (2) Functional enrichment analysis and construction of differential methylation gene regulatory networks were performed. (3) Validation of the two different methylation sites and corresponding gene expression was done in a separate microarray dataset. (4) Correlation analysis was performed on DNA methylation and mRNA expression data.Results: A total of 77 differentially expressed mRNAs matched with differentially methylated genes. Analysis revealed two different methylation sites corresponding to two unique genes—s100a8-cg09174555 and s100a9-cg03165378. Through the verification test of two interesting different expression positions [differentially methylated positions (DMPs)] and their corresponding gene expression, we found that methylation in these genes was negatively correlated to gene expression in the obesity group. Higher S100A8 and S100A9 expressions in obese subjects were validated in a separate microarray dataset.Conclusion: This study confirmed the relationship between DNA methylation and gene expression and emphasized the important role of S100A8 and S100A9 in the pathogenesis of obesity.

scholarly journals Integrated DNA Methylation and Gene Expression Analysis Identified S100A8 and S100A9 in the Pathogenesis of Obesity

2020 ◽  
Author(s):  
Ningyuan Chen ◽  
Liu Miao ◽  
Wei Lin ◽  
Dong-Hua Zhou ◽  
Ling Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Mrna Expression ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Normal Subjects ◽  
Functional Enrichment ◽  
Data Set ◽  
Differentially Methylated Genes

Abstract Background: To explore the association of DNA methylation and gene expression in the pathology of obesity.Methods: (1) Genomic DNA methylation and mRNA expression profile of visceral adipose tissue (VAT) were performed in a comprehensive database of gene expression in obese and normal subjects; (2) functional enrichment analysis and construction of differential methylation gene regulatory network were performed; (3) Validation of the two different methylation sites and corresponding gene expression was done in a separate microarray data set; and (4) correlation analysis was performed on DNA methylation and mRNA expression data.Results: A total of 77 differentially expressed mRNA matched with differentially methylated genes. Analysis revealed two different methylation sites corresponding to two unique genes-s100a8-cg09174555 and s100a9-cg03165378. Through the verification test of two interested different expression positions (DMPS) and their corresponding gene expression, we found that the methylation in these genes was negatively correlated to gene expression in the obesity group. Higher S100A8 and S100A9 expression in obese subjects were validated in a separate microarray data set.Conclusion: This study confirmed the relationship between DNA methylation and gene expression and emphasized the important role of S100A8 and S100A9 in the pathogenesis of obesity.

scholarly journals Ten-gene signature reveals the significance of clinical prognosis and immuno-correlation of osteosarcoma and study on novel skeleton inhibitors regarding MMP9

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weihang Li ◽  
Ziyi Ding ◽  
Dong Wang ◽  
Chengfei Li ◽  
Yikai Pan ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Computational Techniques ◽  
Hub Genes ◽  
Clinical Prognosis ◽  
Lead Compounds

Abstract Objectives This study aimed to identify novel targets in the carcinogenesis, therapy and prognosis of osteosarcoma from genomic level, together with screening ideal lead compounds with potential inhibition regarding MMP-9. Methods Gene expression profiles from GSE12865, GSE14359, GSE33382, GSE36001 and GSE99671 were obtained respectively from GEO database. Differentially expressed genes were identified, and functional enrichment analysis, such as GO, KEGG, GSEA, PPI were performed to make a comprehensive understanding of the hub genes. Next, a series of high-precision computational techniques were conducted to screen potential lead compounds targeting MMP9, including virtual screening, ADME, toxicity prediction, and accurate docking analysis. Results 10 genes, MMP9, CD74, SPP1, CXCL12, TYROBP, FCER1G, HCLS1, ARHGDIB, LAPTM5 and IGF1R were identified as hub genes in the initiation of osteosarcoma. Machine learning, multivariate Cox analysis, ssGSEA and survival analysis demonstrated that these genes had values in prognosis, immune-correlation and targeted treatment. Tow novel compounds, ZINC000072131515 and ZINC000004228235, were screened as potential inhibitor regarding MMP9, and they could bind to MMP9 with favorable interaction energy and high binding affinity. Meanwhile, they were precited to be efficient and safe drugs with low-ames mutagenicity, none weight evidence of carcinogenicity, as well as non-toxic with liver. Conclusions This study revealed the significance of 10-gene signature in the development of osteosarcoma. Besides, drug candidates identified in this study provided a solid basis on MMP9 inhibitors’ development.

scholarly journals Microarray Analysis of Transcriptome of Medulla Identifies Potential Biomarkers for Parkinson’s Disease

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xiao-Yang Liao ◽  
Wei-Wen Wang ◽  
Zheng-Hui Yang ◽  
Jun Wang ◽  
Hang Lin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Motor Nucleus ◽  
Inferior Olivary Nucleus ◽  
Potential Biomarkers

To complement the molecular pathways contributing to Parkinson’s disease (PD) and identify potential biomarkers, gene expression profiles of two regions of the medulla were compared between PD patients and control. GSE19587 containing two groups of gene expression profiles [6 dorsal motor nucleus of the vagus (DMNV) samples from PD patients and 5 from controls, 6 inferior olivary nucleus (ION) samples from PD patients and 5 from controls] was downloaded from Gene Expression Omnibus. As a result, a total of 1569 and 1647 differentially expressed genes (DEGs) were, respectively, screened in DMNV and ION with limma package ofR. The functional enrichment analysis by DAVID server (the Database for Annotation, Visualization and Integrated Discovery) indicated that the above DEGs may be involved in the following processes, such as regulation of cell proliferation, positive regulation of macromolecule metabolic process, and regulation of apoptosis. Further analysis showed that there were 365 common DEGs presented in both regions (DMNV and ION), which may be further regulated by eight clusters of microRNAs retrieved with WebGestalt. The genes in the common DEGs-miRNAs regulatory network were enriched in regulation of apoptosis process via DAVID analysis. These findings could not only advance the understandings about the pathogenesis of PD, but also suggest potential biomarkers for this disease.

scholarly journals Genome‑wide profiling of DNA methylation and gene expression unravel the epigenetic landscape in diabetes-related hypothyroidism

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jingyi Luo ◽  
Xiaoxia Wang ◽  
Li Yuan ◽  
Lixin Guo
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Rna Sequencing ◽  
High Glucose ◽  
Epigenetic Modification ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Normal Medium ◽  
Genome Wide ◽  
Low T3

Abstract Background Type 2 diabetes mellitus (T2DM) and hypothyroidism are two common endocrine diseases and the phenomenon that the prevalence of diabetes-related hypothyroidism shows a significant upward trend deserves further attention, but the specific pathogenesis is not yet clear. The study aimed to explore the molecular mechanisms on DNA methylation regulating gene expression and participating in diabetes-related hypothyroidism through genome-wide DNA methylation and RNA sequencing. Results The prevalence of hypothyroidism in T2DM patients was significantly higher than that in patients without T2DM (P = 0.018). Meanwhile, high TSH and low T3 and T4 levels were detected in diabetic mice. Low T3 and T4 levels were detected in Nthy-ori3-1 cells incubated in high-glucose medium. Differentially expressed genes (DEGs) and differentially methylated regions (DMRs) were detected by RNA sequencing and reduced representation bisulfite sequencing in Nthy-ori3-1 cells cultured in high-glucose and normal medium. Functional enrichment analyses reveled that DMRs and DEGs were related to significant pathways including Ras, Wnt and MAPK pathways. Conclusions We observed the potential connection between T2DM and hypothyroidism. This study was the first one carrying out DNA methylation and gene expression profiles to explore epigenetic modification in diabetes-related hypothyroidism, which provided information for the detailed study of the molecular mechanism in diabetes-related hypothyroidism.

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