scholarly journals Exploring the Functional Disorder and Corresponding Key Transcription Factors in Intraductal Papillary Mucinous Neoplasms Progression

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Guiying Bai ◽  
Chenxuan Wu ◽  
Yingtang Gao ◽  
Guiming Shu
Keyword(s):  
Transcription Factors ◽  
Ductal Carcinoma ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Functional Enrichment Analysis ◽  
Mucinous Carcinoma ◽  
Functional Enrichment ◽  
Functional Disorder ◽  
Similar Expression Pattern

This study has analyzed the gene expression patterns of an IPMN microarray dataset including normal pancreatic ductal tissue (NT), intraductal papillary mucinous adenoma (IPMA), intraductal papillary mucinous carcinoma (IPMC), and invasive ductal carcinoma (IDC) samples. And eight clusters of differentially expressed genes (DEGs) with similar expression pattern were detected byk-means clustering. Then a survey map of functional disorder in IPMN progression was established by functional enrichment analysis of these clusters. In addition, transcription factors (TFs) enrichment analysis was used to detect the key TFs in each cluster of DEGs, and three TFs (FLI1, ERG, and ESR1) were found to significantly regulate DEGs in cluster 1, and expression of these three TFs was validated by qRT-PCR. All these results indicated that these three TFs might play key roles in the early stages of IPMN progression.

scholarly journals Comparative Transcriptome Analysis Suggests Key Roles for 5-Hydroxytryptamlne Receptors in Control of Goose Egg Production

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 455 ◽  
Author(s):  
Qingyuan Ouyang ◽  
Shenqiang Hu ◽  
Guosong Wang ◽  
Jiwei Hu ◽  
Jiaman Zhang ◽  
...  
Keyword(s):  
Egg Production ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Production Performance ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Anser Anser

To date, research on poultry egg production performance has only been conducted within inter or intra-breed groups, while those combining both inter- and intra-breed groups are lacking. Egg production performance is known to differ markedly between Sichuan white goose (Anser cygnoides) and Landes goose (Anser anser). In order to understand the mechanism of egg production performance in geese, we undertook this study. Here, 18 ovarian stromal samples from both Sichuan white goose and Landes goose at the age of 145 days (3 individuals before egg production initiation for each breed) and 730 days (3 high- and low egg production individuals during non-laying periods for each breed) were collected to reveal the genome-wide expression profiles of ovarian mRNAs and lncRNAs between these two geese breeds at different physiological stages. Briefly, 58, 347, 797, 777, and 881 differentially expressed genes (DEGs) and 56, 24, 154, 105, and 224 differentially expressed long non-coding RNAs (DElncRNAs) were found in LLD vs. HLD (low egg production Landes goose vs. high egg production Landes goose), LSC vs. HSC (low egg production Sichuan White goose vs. high egg production Sichuan white goose), YLD vs. YSC (young Landes goose vs. young Sichuan white goose), HLD vs. HSC (high egg production Landes goose vs. high egg production Sichuan white goose), and LLD vs. LSC (low egg production Landes goose vs. low egg production Sichuan white goose) groups, respectively. Functional enrichment analysis of these DEGs and DElncRNAs suggest that the “neuroactive ligand–receptor interaction pathway” is crucial for egg production, and particularly, members of the 5-hydroxytryptamine receptor (HTR) family affect egg production by regulating ovarian metabolic function. Furthermore, the big differences in the secondary structures among HTR1F and HTR1B, HTR2B, and HTR7 may lead to their different expression patterns in goose ovaries of both inter- and intra-breed groups. These results provide novel insights into the mechanisms regulating poultry egg production performance.

scholarly journals Protein expression profiles in Meishan and Duroc sows during mid-gestation reveal differences affecting uterine capacity, endometrial receptivity, and the maternal–fetal Interface

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Kejun Wang ◽  
Kaijie Yang ◽  
Qiao Xu ◽  
Yufang Liu ◽  
Wenting Li ◽  
...  
Keyword(s):  
Protein Expression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Embryonic Mortality ◽  
Functional Enrichment ◽  
Matrix Remodeling ◽  
Swine Industry ◽  
Embryonic Loss

Abstract Background Embryonic mortality is a major concern in the commercial swine industry and primarily occurs early in gestation, but also during mid-gestation (~ days 50–70). Previous reports demonstrated that the embryonic loss rate was significant lower in Meishan than in commercial breeds (including Duroc). Most studies have focused on embryonic mortality in early gestation, but little is known about embryonic loss during mid-gestation. Results In this study, protein expression patterns in endometrial tissue from Meishan and Duroc sows were examined during mid-gestation. A total of 2170 proteins were identified in both breeds. After statistical analysis, 70 and 114 differentially expressed proteins (DEPs) were identified in Meishan and Duroc sows, respectively. Between Meishan and Duroc sows, 114 DEPs were detected at day 49, and 98 DEPs were detected at day 72. Functional enrichment analysis revealed differences in protein expression patterns in the two breeds. Around half of DEPs were more highly expressed in Duroc at day 49 (DUD49), relative to DUD72 and Meishan at day 49 (MSD49). Many DEPs appear to be involved in metabolic process such as arginine metabolism. Our results suggest that the differences in expression affect uterine capacity, endometrial matrix remodeling, and maternal-embryo cross-talk, and may be major factors influencing the differences in embryonic loss between Meishan and Duroc sows during mid-gestation. Conclusions Our data showed differential protein expression pattern in endometrium between Meishan and Duroc sows and provides insight into the development process of endometrium. These findings could help us further uncover the molecular mechanism involved in prolificacy.

scholarly journals Identification of Potential Biomarkers and Related Transcription Factors in Peripheral Blood of Tuberculosis Patients

2020 ◽  
Vol 17 (19) ◽  
pp. 6993
Author(s):  
Longxiang Xie ◽  
Xiaoyu Chao ◽  
Tieshan Teng ◽  
Qiming Li ◽  
Jianping Xie
Keyword(s):  
Transcription Factors ◽  
T Cell Activation ◽  
Peripheral Blood ◽  
Cell Activation ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Peripheral Blood Mononuclear ◽  
Potential Biomarkers

Tuberculosis (TB), one major threat to humans, can infect one third of the worldwide population, and cause more than one million deaths each year. This study aimed to identify the effective diagnosis and therapy biomarkers of TB. Hence, we analyzed two microarray datasets (GSE54992 and GSE62525) derived from the Gene Expression Omnibus (GEO) database to find the differentially expressed genes (DEGs) of peripheral blood mononuclear cell (PBMC) between TB patients and healthy specimens. Functional and pathway enrichment of the DEGs were analyzed by Metascape database. Protein-protein interaction (PPI) network among the DEGs were constructed by STRING databases and visualized in Cytoscape software. The related transcription factors regulatory network of the DEGs was also constructed. A total of 190 DEGs including 36 up-regulated genes and 154 down-regulated genes were obtained in TB samples. Gene functional enrichment analysis showed that these DEGs were enriched in T cell activation, chemotaxis, leukocyte activation involved in immune response, cytokine secretion, head development, etc. The top six hub genes (namely, LRRK2, FYN, GART, CCR7, CXCR5, and FASLG) and two significant modules were got from PPI network of DEGs. Vital transcriptional factors, such as FoxC1 and GATA2, were discovered with close interaction with these six hub DEGs. By systemic bioinformatic analysis, many DEGs associated with TB were screened, and these identified hub DEGs may be potential biomarkers for diagnosis and treatment of TB in the future.

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 Mitochondrial function, fatty acid metabolism, and immune system are relevant features of pig adipose tissue development

2012 ◽  
Vol 44 (22) ◽  
pp. 1116-1124 ◽  
Author(s):  
Annie Vincent ◽  
Isabelle Louveau ◽  
Florence Gondret ◽  
Bénédicte Lebret ◽  
Marie Damon
Keyword(s):  
Adipose Tissue ◽  
Fatty Acid ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Low Grade ◽  
Large White ◽  
Mitochondrial Energy Metabolism

The molecular mechanisms underlying the genetic control of fat development in humans and livestock species still require characterization. To gain insights on gene expression patterns associated with genetic propensity for adiposity, we compared subcutaneous adipose tissue (SCAT) transcriptomics profiles from two contrasted pig breeds for body fatness. Samples were obtained from Large White (LW; lean phenotype) and Basque pigs (B; low growth and high fat content) at 35 kg ( n = 5 per breed) or 145 kg body weight ( n = 10 per breed). Using a custom adipose tissue microarray, we found 271 genes to be differentially expressed between the two breeds at both stages, out of which 123 were highly expressed in LW pigs and 148 genes were highly expressed in B pigs. Functional enrichment analysis based on gene ontology (GO) terms highlighted gene groups corresponding to the mitochondrial energy metabolism in LW pigs, whereas immune response was found significantly enriched in B pigs. Genes associated with lipid metabolism, such as ELOVL6, a gene involved in fatty acid elongation, had a lower expression in B compared with LW pigs. Furthermore, despite enlarged adipocyte diameters and higher plasma leptin concentration, B pigs displayed reduced lipogenic enzyme activities compared with LW pigs at 145 kg. Altogether, our results suggest that the development of adiposity was associated with a progressive worsening of the metabolic status, leading to a low-grade inflammatory state, and may thus be of significant interest for both livestock production and human health.

scholarly journals Bayesian Functional Data Clustering for Temporal Microarray Data

2008 ◽  
Vol 2008 ◽  
pp. 1-4 ◽  
Author(s):  
Ping Ma ◽  
Wenxuan Zhong ◽  
Yang Feng ◽  
Jun S. Liu
Keyword(s):  
Clustering Algorithm ◽  
Enrichment Analysis ◽  
Information Criterion ◽  
Microarray Dataset ◽  
Gene Clusters ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cluster Number ◽  
Mixed Effect ◽  
Temporal Gene Expression

We propose a Bayesian procedure to cluster temporal gene expression microarray profiles, based on a mixed-effect smoothing-spline model, and design a Gibbs sampler to sample from the desired posterior distribution. Our method can determine the cluster number automatically based on the Bayesian information criterion, and handle missing data easily. When applied to a microarray dataset on the budding yeast, our clustering algorithm provides biologically meaningful gene clusters according to a functional enrichment analysis.

scholarly journals Genome-wide phylogenetic analysis, expression pattern, and transcriptional regulatory network of the pig C/EBP gene family

2020 ◽  
Author(s):  
Chaoxin Zhang ◽  
Tao Wang ◽  
Shengwei Liu ◽  
Bing Zhang ◽  
Xue Li ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Rna Seq ◽  
Group I

Abstract Background: The vertebrate C/EBP transcription factors regulate many important biological processes, such as cell proliferation, differentiation, signal transduction, inflammation, and energy metabolism. The first C/EBP protein was identified in rat liver nuclei. Development of sequencing technology resulted in identification of the C/EBP genes in various species. In this study, a bioinformatics approach was used to determine the distribution of the members of the C/EBP family in vertebrates. A phylogenetic tree was constructed to analyze the C/EBP genes in vertebrates. Based on RNA-seq data, the expression patterns of pig C/EBP members in various tissues were analyzed. In addition, a gene transcription regulatory network was constructed with pig C/EBP members as the core.Results: We identified a total of 92 C/EBP genes in 17 vertebrate genomes. Phylogenetic analysis showed that all C/EBP TFs were classified into two groups; group I contained C/EBPβ TFs, and group II contained the remaining C/EBP TFs. The C/EBPα, C/EBPβ, C/EBPδ, C/EBPγ, and C/EBPζ genes were expressed ubiquitously with inconsistent expression patterns in various tissues. Moreover, a pig C/EBP regulatory network was constructed, including C/EBP genes, TFs, and miRNAs. A total of 39 FFL motifs were detected in the pig C/EBP regulatory network. Based on the RNA-seq data, gene expression patterns related to this FFL sub-network were analyzed in 27 adult Duroc tissues. Certain FFL motifs may be tissue specific. Functional enrichment analysis indicated that C/EBP and its target genes are involved in many important biological pathways. Conclusions: These results provide valuable information that clarifies the evolutionary relationships of the C/EBP family and contributes to the understanding of the biological function of C/EBP genes.

scholarly journals Bioinformatics analysis of key biomarkers for retinoblastoma

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110222
Author(s):  
Xin-mei Zhao ◽  
Yuan-Bin Li ◽  
Peng Sun ◽  
Ya-di Pu ◽  
Meng-jie shan ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Protein Protein Interaction ◽  
Bilateral Retinoblastoma ◽  
Endopeptidase Activity ◽  
Novel Biomarkers ◽  
Geo Database

Objective To identify key genes involved in occurrence and development of retinoblastoma. Methods The microarray dataset, GSE5222, was downloaded from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between unilateral and bilateral retinoblastoma were identified and functional enrichment analysis performed. The protein–protein interaction (PPI) network was constructed and analysed by STRING and Cytoscape. Results DEGs were mainly associated with activation of cysteine-type endopeptidase activity involved in apoptotic process and small molecule catabolic process. Seven genes (WAS, GNB3, PTGER1, TACR1, GPR143, NPFF and CDKN2A) were identified as HUB genes. Conclusion Our research provides more understanding of the mechanisms of the disease at a molecular level and may help in the identification of novel biomarkers for retinoblastoma.

scholarly journals Microarray analysis of genes with differential expression of m6A methylation in lung cancer

2021 ◽  
Author(s):  
Shuo Wu ◽  
Xing Lv ◽  
Yan Zhang ◽  
Xi Xu ◽  
Feng Zhao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Differential Expression ◽  
Microarray Analysis ◽  
Fluid Shear Stress ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Tissue Samples ◽  
Mrna Methylation

Purpose: N6-methyladenosine (m6A) is among the most abundant mRNA modifications in eukaryote. The aim of this study was to investigate function of m6A mRNA methylation in lung cancer and the underlying mechanism. Methods: Microarray analysis was performed to detect the differences in RNA expression between cancerous and adjacent non-cancerous tissue samples. The target mRNAs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Hierarchical clustering of RNAs was conducted to identify distinct m6A methylation or expression patterns between the samples. Results: In this study, some differentially expressed genes (DEGs) of mRNAs were identified, including up-regulated SPP1 and down-regulated pRB. Functional enrichment analysis revealed that while differential hypermethylation was related to cell cycle, intracellular part and protein binding, the main pathway involved herpes simplex virus 1 infection related to down-regulated AKT, Araf1 and BCL2A1. In the meantime, sexual reproduction, cohesin complex and portein C-terminus binding was functionally linked to differential hypomethylation, while fluid shear stress and atherosclerosis were identified as the main pathways related to up-regulated GST and CNP. Conclusions: We showed that lung cancer development involved differential expression of SPP1 and pRB mRNA, as well as m6A mRNA methylation in AKT, APAF1, BCL2A1, GST and CNP genes.

Single-cell RNA-sequencing Unravels Heterogeneity of Cardiomyocytes and Signaling Pathways in Pressure Overload

2020 ◽  
Author(s):  
Rongjun Zou ◽  
Wanting Shi ◽  
Minghui Zou ◽  
Weidan Chen ◽  
Wenlei Li ◽  
...  
Keyword(s):  
Cardiac Hypertrophy ◽  
Mouse Models ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Cell Clusters ◽  
Single Cell Rna Sequencing

Abstract Background This study aimed to unravel the heterogeneity of cardiomyocytes and probed out hub genes and hub pathways for cardiac hypertrophy based on transverse aortic constriction (TAC) mouse models using single-cell RNA sequencing (scRNA-seq). Methods scRNA-seq data of TAC mouse models were retrieved from the GSE95140 dataset. After filtering, cell clusters were detected using scRNA-seq data, followed by identification of differentially expressed genes (DEGs). Then, functional enrichment analysis of DEGs was presented. GSVA scores of hub pathways were calculated. After that, hub genes were detected by protein-protein interaction (PPI) network and expression association analysis. Cell subtypes were clustered using UMAP and the expression patterns of hub genes across different cell subtypes and different stages of cardiac hypertrophy were visualized. Finally, hub genes and hub pathways were verified using the GSE76 and GSE36074 datasets. Results Following data filtering and normalization, 3408 DEGs were identified between TAC and sham operation. As shown functional enrichment analysis, hub pathways were identified including cardiac hypertrophy, ion transport, myocardial remodeling, apoptosis, HIF pathway and metabolise. Eight hub genes (Vldlr, Ugp2, Tgm2, Pygm, Flnc, Ctsd, Clu and Atp1b1) with the highest degree in the PPI network and the strongest correlation with GSVA calculated score of hub pathways were identified for cardiac hypertrophy. Six cell subtypes were clustered, composed of fibroblast, CM-A, CM-V, trabecular CM and endothelial cell. There was a distinct heterogeneity in the expression patterns of hub genes and the GSVA scores of hub pathways across different cell clusters and different stages of cardiac hypertrophy. The hub genes and hub pathways were externally verified by the two independent datasets. Conclusion Our findings identified hub genes and hub pathways for cardiac hypertrophy, which had a distinct heterogeneity across different cell clusters and different stages of cardiac hypertrophy.

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