scholarly journals Transcriptome Analysis Reveals the Genes Involved in Growth and Metabolism in Muscovy Ducks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xingxin Wang ◽  
Yingping Xiao ◽  
Hua Yang ◽  
Lizhi Lu ◽  
Xiuting Liu ◽  
...  
Keyword(s):  
Transcriptome Analysis ◽  
Enrichment Analysis ◽  
Vital Role ◽  
Glutathione Metabolism ◽  
Protein Digestion ◽  
Real Time Quantitative Pcr ◽  
Go Enrichment ◽  
Muscovy Ducks ◽  
Ileal Tissue ◽  
Go Enrichment Analysis

Muscovy ducks are among the best meat ducks in the world. The objective of this study was to identify genes related to growth metabolism through transcriptome analysis of the ileal tissue of Muscovy ducks. Duck ileum samples with the highest (H group, n = 5 ) and lowest (L group, n = 5 ) body weight were selected from two hundred 70-day-old Muscovy ducks for transcriptome analysis by RNA sequencing. In the screening of differentially expressed genes (DEGs) between the H and L groups, a total of 602 DEGs with a fold change no less than 2 were identified, among which 285 were upregulated and 317 were downregulated. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that glutathione metabolism, pyrimidine metabolism, and protein digestion and absorption processes played a vital role in regulating growth and metabolism. The results showed that 7 genes related to growth and metabolism, namely, ANPEP, ENPEP, UPP1, SLC2A2, SLC6A19, NME4, and LOC106034733, were significantly expressed in group H, which was consistent with the phenotype results. The validation of these 7 genes using real-time quantitative PCR results indicated that the expression level of ENPEP was significantly different between the H and L groups ( P < 0.05 ). This study provides a theoretical basis for exploring the influence of the ileum on growth and metabolism in ducks.

scholarly journals Tissue-Specific Transcriptome Analysis Reveals Candidate Transcripts Associated with the Process of Programmed B Chromosome Elimination in Aegilops speltoides

2020 ◽  
Vol 21 (20) ◽  
pp. 7596
Author(s):  
Anastassia Boudichevskaia ◽  
Alevtina Ruban ◽  
Johannes Thiel ◽  
Anne Fiebig ◽  
Andreas Houben
Keyword(s):  
Transcriptome Analysis ◽  
Enrichment Analysis ◽  
Chromosome Elimination ◽  
B Chromosome ◽  
Aegilops Speltoides ◽  
Transcript Isoforms ◽  
Meristematic Cells ◽  
Go Enrichment ◽  
Laser Capture ◽  
Go Enrichment Analysis

Some eukaryotes exhibit dramatic genome size differences between cells of different organs, resulting from programmed elimination of chromosomes. Here, we present the first transcriptome analysis of programmed chromosome elimination using laser capture microdissection (LCM)-based isolation of the central meristematic region of Aegilops speltoides embryos where B chromosome (B) elimination occurs. The comparative RNA-seq analysis of meristematic cells of embryos with (Bplus) and without Bs (B0) allowed the identification of 14,578 transcript isoforms (35% out of 41,615 analyzed transcript isoforms) that are differentially expressed during the elimination of Bs. A total of 2908 annotated unigenes were found to be up-regulated in Bplus condition. These genes are either associated with the process of B chromosome elimination or with the presence of B chromosomes themselves. GO enrichment analysis categorized up-regulated transcript isoforms into 27 overrepresented terms related to the biological process, nine terms of the molecular function aspect and three terms of the cellular component category. A total of 2726 annotated unigenes were down-regulated in Bplus condition. Based on strict filtering criteria, 341 B-unique transcript isoforms could be identified in central meristematic cells, of which 70 were functionally annotated. Beside others, genes associated with chromosome segregation, kinetochore function and spindle checkpoint activity were retrieved as promising candidates involved in the process of B chromosome elimination.

scholarly journals Transcriptome Analysis Reveals Genes of Flooding-Tolerant and Flooding-Sensitive Rapeseeds Differentially Respond to Flooding at the Germination Stage

Plants ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 693
Author(s):  
Jijun Li ◽  
Sidra Iqbal ◽  
Yuting Zhang ◽  
Yahui Chen ◽  
Zengdong Tan ◽  
...  
Keyword(s):  
Transcriptome Analysis ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Flooding Stress ◽  
Go Enrichment ◽  
The Common ◽  
Go Enrichment Analysis ◽  
Insight Into ◽  
Germination Stage

Flooding results in significant crop yield losses due to exposure of plants to hypoxic stress. Various studies have reported the effect of flooding stress at seedling establishment or later stages. However, the molecular mechanism prevailing at the germination stage under flooding stress remains enigmatic. The present study highlights the comparative transcriptome analysis in two rapeseed lines, i.e., flooding-tolerant (Santana) and -sensitive (23651) lines under control and 6-h flooding treatments at the germination stage. A total of 1840 up-regulated and 1301 down-regulated genes were shared by both lines in response to flooding. There were 4410 differentially expressed genes (DEGs) with increased expression and 4271 DEGs with reduced expression shared in both control and flooding conditions. Gene ontology (GO) enrichment analysis revealed that “transcription regulation”, “structural constituent of cell wall”, “reactive oxygen species metabolic”, “peroxidase”, oxidoreductase”, and “antioxidant activity” were the common processes in rapeseed flooding response. In addition, the processes such as “hormone-mediated signaling pathway”, “response to organic substance response”, “motor activity”, and “microtubule-based process” are likely to confer rapeseed flooding resistance. Mclust analysis clustered DEGs into nine modules; genes in each module shared similar expression patterns and many of these genes overlapped with the top 20 DEGs in some groups. This work provides a comprehensive insight into gene responses and the regulatory network in rapeseed flooding stress and provides guidelines for probing the underlying molecular mechanisms in flooding resistance.

scholarly journals Selenium Treatment Enhanced Clearance of Salmonella in Chicken Macrophages (HD11)

Antioxidants ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 532 ◽  
Author(s):  
Zhexi Liu ◽  
Jianwei Huang ◽  
Yijuan Nie ◽  
Izhar Qazi ◽  
Yutao Cao ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Glutathione Metabolism ◽  
Rna Seq ◽  
Apoptotic Signaling ◽  
Go Enrichment ◽  
Selenium Treatment ◽  
Underlying Mechanisms ◽  
Go Enrichment Analysis

As an important micronutrient, selenium (Se) plays many essential roles in immune response and protection against pathogens in humans and animals, but underlying mechanisms of Se-based control of salmonella growth within macrophages remain poorly elucidated. In this study, using RNA-seq analyses, we demonstrate that Se treatment (at an appropriate concentration) can modulate the global transcriptome of chicken macrophages HD11. The bioinformatic analyses (KEGG pathway analysis) revealed that the differentially expressed genes (DEGs) were mainly enriched in retinol and glutathione metabolism, revealing that Se may be associated with retinol and glutathione metabolism. Meanwhile, Se treatment increased the number of salmonella invading the HD11 cells, but reduced the number of salmonella within HD11 cells, suggesting that enhanced clearance of salmonella within HD11 cells was potentially modulated by Se treatment. Furthermore, RNA-seq analyses also revealed that nine genes including SIVA1, FAS, and HMOX1 were differentially expressed in HD11 cells infected with salmonella following Se treatment, and GO enrichment analysis showed that these DEGs were mainly enriched in an extrinsic apoptotic signaling pathway. In summary, these results indicate that Se treatment may not only affect retinol and glutathione metabolism in macrophages, but could also inhibit salmonella-induced macrophage apoptosis via an extrinsic apoptotic signaling pathway involving SIVA1.

Transcriptome analysis reveals genes potentially related to high fiber strength in a Gossypium hirsutum line IL9 with Gossypium mustelinum introgression

Genome ◽  
2021 ◽  
pp. 1-11
Author(s):  
Qi Chen ◽  
Wei Wang ◽  
Sameer Khanal ◽  
Jinlei Han ◽  
Mi Zhang ◽  
...  
Keyword(s):  
Transcriptome Analysis ◽  
Fiber Strength ◽  
Fiber Quality ◽  
Enrichment Analysis ◽  
Introgression Line ◽  
Recurrent Parent ◽  
High Fiber ◽  
Go Enrichment ◽  
Annotation Information ◽  
Cotton Fiber Quality

Cotton (Gossypium L.) is the most important fiber crop worldwide. Here, transcriptome analysis was conducted on developing fibers of a G. mustelinum introgression line, IL9, and its recurrent parent, PD94042, at 17 and 21 days post-anthesis (dpa). Differentially expressed genes (DEGs) of PD94042 and IL9 were identified. Gene Ontology (GO) enrichment analysis showed that the annotated DEGs were rich in two main biological processes and two main molecular functions. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis likewise showed that the annotated DEGs were mainly enriched in metabolic pathways and biosynthesis of secondary metabolites. In total, 52 DEGs were selected as candidate genes based on comparison of the DEGs and GO function annotation information. Quantitative real-time PCR (RT-qPCR) analysis results for 12 randomly selected DEGs were consistent with transcriptome analysis. SNP identification based on G. mustelinum chromatin segment introgression showed that 394 SNPs were identified in 268 DEGs, and two genes with known functions were identified within fiber strength quantitative trait loci (QTL) regions or near the confidence intervals. We identified 52 key genes potentially related to high fiber strength in a G. mustelinum introgression line and provided significant insights into the study of cotton fiber quality improvement.

scholarly journals Comprehensive biological information analysis of PTEN gene in pan-cancer

2021 ◽  
Author(s):  
Hang Zhang ◽  
Wenhan Zhou ◽  
Xiaoyi Yang ◽  
Shuzhan Wen ◽  
Baicheng Zhao ◽  
...  
Keyword(s):  
3D Structure ◽  
Enrichment Analysis ◽  
Biological Information ◽  
Pten Gene ◽  
Clinical Prognosis ◽  
Regulatory Pathways ◽  
Go Enrichment ◽  
Go Enrichment Analysis ◽  
Pan Cancer ◽  
Low Expression

Abstract Background PTEN is a multifunctional tumor suppressor gene mutating at high frequency in a variety of cancers. However, its expression in pan-cancer, correlated genes, survival prognosis, and regulatory pathways are not completely described. Here, we aimed to conduct a comprehensive analysis from the above perspectives in order to provide reference for clinical application. Methods we studied the expression levels in cancers by using data from TCGA and GTEx database. Obtain expression box plot from UALCAN database. Perform mutation analysis on the cBioportal website. Obtain correlation genes on the GEPIA website. Construct protein network and perform KEGG and GO enrichment analysis on the STRING database. Perform prognostic analysis on the Kaplan-Meier Plotter website. We also performed transcription factor prediction on the PROMO database and performed RNA-RNA association and RNA-protein interaction on the RNAup Web server and RPISEq. The gene 3D structure, protein sequence and conserved domain were obtained in NCBI respectively. Results PTEN was underexpressed in all cancers we studied. It was closely related to the clinical stage of tumors, suggesting PTEN may involved in cancer development and progression. The mutations of PTEN were present in a variety of cancers, most of which were truncation mutations and missense mutations. Among cancers (KIRC, LUAD, THYM, UCEC, Gastric Cancer, Liver Cancer, Lung Cancer, Breast Cancer), patients with low expression of PTEN had a shorter OS time and poorer OS prognosis. The low expression of PTEN can cause the deterioration of RFS in certain cancers (TGCT, UCEC, LIHC, LUAD, THCA), suggesting that the expression of PTEN was related to the clinical prognosis. Our study identified genes correlated with PTEN and performed GO enrichment analysis on 100 PTEN-related genes obtained from the GEPIA website. Conclusions The understanding of PTEN gene and the in-depth exploration of its related regulatory pathways may provide insight for the discovery of tumor-specific biomarkers and clinical potential therapeutic targets.

Transcriptome Signatures Reveal Candidate Key Genes in the Peripheral Blood Mononuclear Cells of Patients With Coronary Artery Disease

2020 ◽  
Author(s):  
Vijayakrishna Kolur ◽  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti ◽  
Anandkumar Tengli
Keyword(s):  
Coronary Artery Disease ◽  
Gene Ontology ◽  
Coronary Artery ◽  
Regulatory Network ◽  
Target Gene ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Go Enrichment ◽  
Artery Disease ◽  
Go Enrichment Analysis

Abstract BackgroundCoronary artery disease (CAD) is one of the most common disorders in the cardiovascular system. This study aims to explore potential signaling pathways and important biomarkers that drive CAD development. MethodsThe CAD GEO Dataset GSE113079 was featured to screen differentially expressed genes (DEGs). The pathway and Gene Ontology (GO) enrichment analysis of DEGs were analyzed using the ToppGene. We screened hub and target genes from protein-protein interaction (PPI) networks, target gene - miRNA regulatory network and target gene - TF regulatory network, and Cytoscape software. Validations of hub genes were performed to evaluate their potential prognostic and diagnostic value for CAD. Results1,036 DEGs were captured according to screening criteria (525upregulated genes and 511downregulated genes). Pathway and Gene Ontology (GO) enrichment analysis of DEGs revealed that these up and down regulated genes are mainly enriched in thyronamine and iodothyronamine metabolism, cytokine-cytokine receptor interaction, nervous system process, cell cycle and nuclear membrane. Hub genes were validated to find out potential prognostic biomarkers, diagnostic biomarkers and novel therapeutic target for CAD. ConclusionsIn summary, our findings discovered pivotal gene expression signatures and signaling pathways in the progression of CAD. CAPN13, ACTBL2, ERBB3, GATA4, GNB4, NOTCH2, EXOSC10, RNF2, PSMA1 and PRKAA1 might contribute to the progression of CAD, which could have potential as biomarkers or therapeutic targets for CAD.

scholarly journals Mining Featured Biomarkers Linked with Epithelial Ovarian Cancer Based on Bioinformatics

Diagnostics ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 39
Author(s):  
◽  
Chanabasayya Vastrad ◽  
◽  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Gene ◽  
Enrichment Analysis ◽  
Microrna Target ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Gene Regulatory ◽  
Go Enrichment Analysis

: Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors ) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C) , VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC.

scholarly journals A Network Pharmacology Approach to Explore the Pharmacological Mechanism of Xiaoyao Powder on Anovulatory Infertility

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Huiping Liu ◽  
Liuting Zeng ◽  
Kailin Yang ◽  
Guomin Zhang
Keyword(s):  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Signal Pathways ◽  
Active Compounds ◽  
Pharmacological Mechanism ◽  
Go Enrichment ◽  
Human Proteins ◽  
Network Pictures ◽  
Go Enrichment Analysis ◽  
Anovulatory Infertility

Aim.To explore the pharmacological mechanism of Xiaoyao powder (XYP) on anovulatory infertility by a network pharmacology approach.Method.Collect XYP’s active compounds by traditional Chinese medicine (TCM) databases, and input them into PharmMapper to get their targets. Then note these targets by Kyoto Encyclopedia of Genes and Genomes (KEGG) and filter out targets that can be noted by human signal pathway. Get the information of modern pharmacology of active compounds and recipe’s traditional effects through databases. Acquire infertility targets by Therapeutic Target Database (TTD). Collect the interactions of all the targets and other human proteins via String and INACT. Put all the targets into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to do GO enrichment analysis. Finally, draw the network by Cytoscape by the information above.Result.Six network pictures and two GO enrichment analysis pictures are visualized.Conclusion.According to this network pharmacology approach some signal pathways of XYP acting on infertility are found for the first time. Some biological processes can also be identified as XYP’s effects on anovulatory infertility. We believe that evaluating the efficacy of TCM recipes and uncovering the pharmacological mechanism on a systematic level will be a significant method for future studies.

scholarly journals The DOMINO web-server for active module identification analysis

2021 ◽  
Author(s):  
Hagai Levi ◽  
Nima Rahmanian ◽  
Ran Elkon ◽  
Ron Shamir
Keyword(s):  
Gene Network ◽  
Enrichment Analysis ◽  
Molecular Processes ◽  
Activity Profile ◽  
Module Identification ◽  
Go Enrichment ◽  
Downstream Analysis ◽  
User Friendly ◽  
Identification Analysis ◽  
Go Enrichment Analysis

Active module identification (AMI) is an essential step in many omics analyses. Such algorithms receive a gene network and a gene activity profile as input and report subnetworks that show significant over-representation of accrued activity signal ("active modules"). Such modules can point out key molecular processes in the analyzed biological conditions. We recently introduced a novel AMI algorithm called DOMINO, and demonstrated that it detects active modules that capture biological signals with markedly improved rate of empirical validation. Here, we provide an online server that executes DOMINO, making it more accessible and user-friendly. To help the interpretation of solutions, the server provides GO enrichment analysis, module visualizations, and accessible output formats for customized downstream analysis. It also enables running DOMINO with various gene identifiers of different organisms. The server is available at http://domino.cs.tau.ac.il. Its codebase is available at https://github.com/Shamir-Lab.

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