scholarly journals Selection of Cashmere Fineness Functional Genes by Translatomics

2022 ◽  
Vol 12 ◽  
Author(s):  
Yu Zhang ◽  
Dongyun Zhang ◽  
Yanan Xu ◽  
Yuting Qin ◽  
Ming Gu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Enrichment Analysis ◽  
Phenotypic Traits ◽  
Functional Region ◽  
Papillomavirus Infection ◽  
Extracellular Region ◽  
Genome Wide ◽  
Go Enrichment ◽  
Go Enrichment Analysis ◽  
Differential Genes

Cashmere fineness is an important index to evaluate cashmere quality. Liaoning Cashmere Goat (LCG) has a large cashmere production and long cashmere fiber, but its fineness is not ideal. Therefore, it is important to find genes involved in cashmere fineness that can be used in future endeavors aiming to improve this phenotype. With the continuous advancement of research, the regulation of cashmere fineness has made new developments through high-throughput sequencing and genome-wide association analysis. It has been found that translatomics can identify genes associated with phenotypic traits. Through translatomic analysis, the skin tissue of LCG sample groups differing in cashmere fineness was sequenced by Ribo-seq. With these data, we identified 529 differentially expressed genes between the sample groups among the 27197 expressed genes. From these, 343 genes were upregulated in the fine LCG group in relation to the coarse LCG group, and 186 were downregulated in the same relationship. Through GO enrichment analysis and KEGG enrichment analysis of differential genes, the biological functions and pathways of differential genes can be found. In the GO enrichment analysis, 491 genes were significantly enriched, and the functional region was mainly in the extracellular region. In the KEGG enrichment analysis, the enrichment of the human papillomavirus infection pathway was seen the most. We found that the COL6A5 gene may affect cashmere fineness.

scholarly journals Dimethyl itaconate alleviates the pyroptosis of macrophages through oxidative stress

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shan-Shan Huang ◽  
Dong-Yang Guo ◽  
Bing-Bing Jia ◽  
Guo-Long Cai ◽  
Jing Yan ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Quantitative Polymerase Chain Reaction ◽  
Enrichment Analysis ◽  
Go Enrichment ◽  
Polymerase Chain ◽  
Stress Related Genes ◽  
Go Enrichment Analysis ◽  
Necrosis Factor ◽  
Differential Genes ◽  
Dimethyl Itaconate

AbstractMacrophages are involved in the pathophysiology of many diseases as critical cells of the innate immune system. Pyroptosis is a form of macrophage death that induces cytokinesis of phagocytic substances in the macrophages, thereby defending against infection. Dimethyl itaconate (DI) is an analog of itaconic acid with anti-inflammatory effects. However, the effect of dimethyl itaconate on macrophage pyroptosis has not been elucidated clearly. Thus, the present study aimed to analyze the effect of DI treatment on a macrophage pyroptosis model (Lipopolysaccharide, LPS + Adenosine Triphosphate, ATP). The results showed that 0.25 mM DI ameliorated macrophage pyroptosis and downregulated interleukin (IL)-1β expression. Then, real-time quantitative polymerase chain reaction (RT-qPCR) was used to confirm the result of RNA-sequencing of the upregulated oxidative stress-related genes (Gclc and Gss) and downregulated inflammation-related genes (IL-12β and IL-1β). In addition, Gene Ontology (GO) enrichment analysis showed that differential genes were associated with transcript levels and DNA replication. Kyoto encyclopedia of genes and genomes (KEGG) enrichment showed that signaling pathways, such as tumor necrosis factor (TNF), Jak, Toll-like receptor and IL-17, were altered after DI treatment. N-acetyl-L-cysteine (NAC) reversed the DI effect on the LPS + ATP-induced macrophage pyroptosis and upregulated the IL-1β expression. Oxidative stress-related protein Nrf2 is involved in the DI regulation of macrophage pyroptosis. Taken together, these findings suggested that DI alleviates the pyroptosis of macrophages through oxidative stress.

scholarly journals Transcriptome Profiling of Mouse Brain and Lung under Dip2A Regulation Using RNA-Sequencing

2019 ◽  
Author(s):  
Rajiv kumar sah ◽  
Anlan Yang ◽  
Fatoumata Binta Bah ◽  
Salah Adlat ◽  
Ameer Ali Bohio ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Neuronal Cell ◽  
Enrichment Analysis ◽  
Transcriptome Profiling ◽  
Transcriptional Analysis ◽  
Reference Sequence ◽  
Wild Type ◽  
Genome Wide ◽  
Go Enrichment ◽  
Go Enrichment Analysis

AbstractDisconnected interacting 2 homolog A (DIP2A) gene is highly expressed in nervous system and respiratory system of developing embryos. However, genes regulated by Dip2a in developing brain and lung have not been systemically studied. Transcriptome of brain and lung in embryonic 19.5 day (E19.5) were compared between wild type and Dip2a-/- mice. Total RNAs were extracted from brain and lung of E19.5 embryos for RNA-Seq. Clean reads were mapped to mouse reference sequence (mm9) using Tophat and assembled into transcripts by Cufflinks. Edge R and DESeq were applied to identify differentially expressed genes (DEGs) and annotated under GO, COG, KEGG and TF. An average of 50 million reads per sample was mapped to the reference sequence. A total of 214 DEGs were detected in brain (82 up and 132 down) and 1900 DEGs in lung (1259 up and 641 down). GO enrichment analysis indicated that DEGs in both Brain and Lung were mainly enriched in biological processes ‘DNA-templated transcription and Transcription from RNA polymerase II promoter’, ‘multicellular organism development’, ‘cell differentiation’ and ‘apoptotic process’. In addition, COG classification showed that both were mostly involved in ‘Replication, Recombination and Repair’, ‘Signal transduction and mechanism’, ‘Translation, Ribosomal structure and Biogenesis’ and ‘Transcription’. KEGG enrichment analysis showed that brain was mainly enriched in ‘Thryoid cancer’ pathway whereas lung in ‘Complement and Coagulation Cascades’ pathway. Transcription factor (TF) annotation analysis identified Zinc finger domain containing (ZF) proteins were mostly regulated in lung and brain. Interestingly, study identified genes Skor2, Gpr3711, Runx1, Erbb3, Frmd7, Fut10, Sox11, Hapln1, Tfap2c and Plxnb3 from brain that play important roles in neuronal cell maturation, differentiation and survival; genes Hoxa5, Eya1, Ctsh, Erff1, Lama1, Lama2, Rspo2, Sox11, Spry4, Shh, Igf1 and Wnt7a from lung are important in lung development and morphogenesis. Expression levels of the candidate genes were validated by qRT-PCR. Genome wide transcriptional analysis using wild type and Dip2a knockout mice in brain and lung at embryonic day 19.5 (E19.5) provided a genetic basis of molecular function of these genes.

scholarly journals Transcriptomic analysis of nonylphenol effect on Saccharomyces cerevisiae

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10794
Author(s):  
Ceyhun Bereketoglu ◽  
Gozde Nacar ◽  
Tugba Sari ◽  
Bulent Mertoglu ◽  
Ajay Pradhan
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Adaptive Response ◽  
Cell Cycle Progression ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Yeast Cells ◽  
Genome Wide ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Nonylphenol (NP) is a bioaccumulative environmental estrogen that is widely used as a nonionic surfactant. We have previously examined short-term effects of NP on yeast cells using microarray technology. In the present study, we investigated the adaptive response of Saccharomyces cerevisiae BY4742 cells to NP exposure by analyzing genome-wide transcriptional profiles using RNA-sequencing. We used 2 mg/L NP concentration for 40 days of exposure. Gene expression analysis showed that a total of 948 genes were differentially expressed. Of these, 834 genes were downregulated, while 114 genes were significantly upregulated. GO enrichment analysis revealed that 369 GO terms were significantly affected by NP exposure. Further analysis showed that many of the differentially expressed genes were associated with oxidative phosphorylation, iron and copper acquisition, autophagy, pleiotropic drug resistance and cell cycle progression related processes such as DNA and mismatch repair, chromosome segregation, spindle checkpoint activity, and kinetochore organization. Overall, these results provide considerable information and a comprehensive understanding of the adaptive response to NP exposure at the gene expression level.

scholarly journals The effects of the Xijiao Dihuang decoction combined with Yinqiao powder on miRNA-mRNA profiles in mice infected with influenza a virus

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ke Li ◽  
Xiaoming Chen ◽  
Jing Zhong ◽  
Hehe Ye ◽  
Shujing Zhang ◽  
...  
Keyword(s):  
Influenza A ◽  
High Throughput Sequencing ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Kegg Database ◽  
Qrt Pcr ◽  
Go Enrichment ◽  
Differentially Expressed Mirnas ◽  
Therapeutic Mechanisms ◽  
Go Enrichment Analysis

Abstract Background MicroRNAs (miRNAs) play vital roles in acute inflammatory and antiviral responses during influenza A virus (IAV) infection. The Xijiao Dihuang decoction combined with Yinqiao powder (XDY) is applied to remedy viral pneumonia in China and its therapeutic efficacy in pneumonic mice challenged with IAV was demonstrated; however, the underlying mechanisms remain elusive. Thus, this study aimed to explore the miRNA-mRNA profiles in the lungs of IAV-infected mice and investigate the therapeutic mechanisms of XDY involving miRNAs and associated pathways. Methods We detected the cellular miRNA contents in the lungs of mice treated with XDY (23 g/kg/d) for A/FM/1/47 (H1N1) (FM1) infection at 4 days postinoculation (dpi) and 7 dpi. MiRNA and mRNA high-throughput sequencing analyses, and miRNA and mRNA qRT-PCR analyses were used to detect and verify the relevant miRNAs and mRNAs. Conjoint analysis, GO enrichment analysis, and KEGG database analysis were applied to identify the miRNA-mRNA regulatory relationships. Results The quantities of differentially expressed miRNAs and mRNAs were upregulated over time. The data showed that 104 miRNAs and 3485 mRNAs were differentially expressed after challenge with FM1 on day 4, while 191 miRNAs and 6126 mRNAs were differentially expressed on day 7. The GO enrichment analysis and KEGG database data showed that the differentially expressed miRNAs and mRNAs were mainly enriched in JNK activity, MAPK phosphatase activity, and the TLR, Jak-STAT and TNF signalling pathways after treatment of FM1 infection with XDY. Generally, the expression trends of differentially expressed miRNAs and mRNAs based on the qRT-PCR results exhibited good consistency with the results of the high-throughput sequencing analysis. Conclusions MiRNAs and mRNAs were differentially expressed during FM1 infection. The therapeutic mechanisms of XDY in FM1-infected mice, might be related to regulating antiviral immunity and ameliorating excessive inflammatory responses by modulating the expression of dysregulated miRNAs and mRNAs involved in the ERK/JNK-AP-1, and IFN-β/STAT signalling pathways.

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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