Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Next Generation Sequencing Facilitates Quantitative Analysis of Macrophage (RAW264.7) with and without arctigenin treatment

ABSTRACT: Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to explore the mechanism and target of arctigenin affecting macrophages (RAW264.7) Methods: Total mRNA was extracted from cells using an RNA extraction kit (Qiagen K.K., Tokyo, Japan). RNA-Seq was performed using HiSeq X Ten for next-generation sequencing by OE Biotech. Co., Ltd. (Shanghai, China). Sequenced reads were mapped to the mm9 genome using hisat2.The RNA-Seq reads that were aligned against the exon regions of genes were quantified with HTSeq-Count in the RefSeq mm9 annotation. GSEA was performed on mRNA expression datasets using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Gene signatures were considered enriched if the false discovery rate (FDR) q-values and the family-wise error rate (FWER) p-values show significant difference. Result: The GSEA from the KEGG database indicated that AG-induced biological processes involved several inflammatory process. GSEA methods on immunologic gene sets of Molecular Signatures Database show that the gene set of NFAT5 knockout passed the filtering criteria (p<0.01) Conclusion: NFAT5 was the target of arctigenin

ORGANISM(S): Mus musculus

PROVIDER: GSE127902 | GEO | 2019/03/07

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

CpG island mediated linear and spatial gene partitioning (RNA-seq)

Project description:We reanalyzed published RNA-seq data to study 1) the genomic landscapes near surrounding regions of transcriptional start sites with regard to the gene expression activities and 2) the gene expression change upon transcription factor (MYBL1, ATF4) depletion. Raw data were downloaded from Sequence Read Archive (SRA) in National Center for Biotechnology Information (NCBI) database. FASTQ files were extracted with the SRA Toolkit version 2.5.5 and aligned using STAR 2.4.2 onto the mouse and human genome (mm9 and hg19, respectively). Gene expression was calculated as RPKM values using rpkmforgenes.py (Ramsköld et al., 2009).

2018-02-15 | GSE80793 | GEO

Genotyping of E14 mouse embryonic stem cells by sequencing

Project description:More than 2x10E9 sequences made on Illumina platform derived from the genome of E14 embryonic stem cells cultured in our laboratory were used to build a database of about 2.7x10E6 single nucleotide variant. The database was validated using other two sequencing datasets from other laboratory and high overlap was observed. The identified variant are enriched on intergenic regions, but several thousands reside on gene exons and regulatory regions, such as promoters, enhancers, splicing site and untranslated regions of RNA, thus indicating high probability of an important functional impact on the molecular biology of this cells. We created a new E14 genome assembly including the new identified variants and used it to map reads from next generation sequencing data generated in our laboratory or in others on E14 cell line. We observed an increase in the number of mapped reads of about 5%. CpG dinucleotide showed the higher variation frequency, probably because of it could be target of DNA methylation. We performed a reduced representation bisulfite sequencing on E14 cell line to test our new genome assembly with respect to the mm9 genome reference. After mapping and methylation status calling, we obtained an increase of about 120,000 called CpG and we avoided about 20,000 wrong CpG calling. genotyping of E14 embryonic stem cells (ESCs) and Reduced representation Bisulfite Sequencing (RRBS) of E14 ESCs.

2014-07-10 | E-GEOD-53149 | biostudies-arrayexpress

Genotyping of E14 mouse embryonic stem cells by sequencing

2014-07-10 | GSE53149 | GEO

Rbfox2-coordinated alternative splicing of Mef2d and Rock2 controls myoblast fusion during myogenesis

Project description:Purpose: The goals of this study are to compare transcriptomes using RNA-seq of mouse myoblasts (C2C12 cell line) in undifferentiated and differentiated states and with siRNA-mediated knock down of the RNA binding proteins, Rbfox1 (only expressed in differentiated state) and Rbfox2 (expressed in both undifferentiated and differentiated states). Methods: Differentiated and undifferentiated C2C12 cultures treated with Rbfox1 (differentiated only) or Rbfox2 siRNAs or a mock siRNA transfection were used for RNA-Seq analysis using Illumina HiSeq2000. 101x2 paired-end RNA-seq reads were first uniquely aligned to the mouse genome (mm9) using TopHat 1.4.1. RSEM was used to count the number of reads mapped to genes using UCSC database, followed by edgeR to call differentially expressed genes with false discovery rate less than 0.01. Cufflinks was used to reconstruct isoforms and analyze alternative splicing and percent spliced in (PSI) was calculated. PSI values were validated by RT-PCR. Results: 58-88% of the RNA-seq reads from technical and biological replicates mapped uniquely to the mouse genome. Analysis of gene expression and alternative splicing changes are published in Singh et al. Molecular Cell (2014). Conclusions: Our study has identified gene expression and alternative splicing transitions that occur during myoblast differentiation, demonstrate that 30% of the splicing transitions are regulated by Rbfox2, demonstrated that Rbfox2 is required for a late step of myoblast differentiation and identified two Rbfox2-regulated splicing transitions that are required for differentiation.

2014-07-15 | GSE58928 | GEO

Next Generation Sequencing Facilitates Quantitative Analysis of the MDM2i and WIPi Combination Therapy in Intrahepatic Cholangiocarcinoma Transcriptomes

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized the systems-based analysis of cellular pathways. The purpose of this study is to compare the expression profiles of single and combination treatment of MDM2i and WIP1i in RBE cells by NGS-derived transcriptome profiling (RNA-seq). Methods: RBE mRNA profiles of HDM201 (MDM2i) and/or GSK2830371 (WIP1i) treatment for 6 or 24 hours were generated by deep sequencing, in triplicate, using Illumina NextSeq sequencer. The sequence of each read was trimmed based on the quality score (Q30), and read lengths less than 45 bp were dis-carded prior to further analysis. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: STAR aligner followed by ANOVA. Results: We mapped about 30 million sequence reads per sample to the RBE transcriptomes after HDM201 and/or GSK2830371 treatment. The differentially expressed genes were selected for more than 2-fold changes and p-values for differences in expression of less than 0.05. From four different comparisons, HDM201 alone and in combination with GSK2830371 for 6 h and 24 h, 26 consistently altered genes were identified (21 upregulated genes and 5 downregulated genes). Conclusions: Our study represents the detailed analysis of MDM2i and/or WIPi therapy in intrahepatic cholangiocarcinoma transcriptomes. Differentially expressed genes from combination treatment were enriched for genes involved in p53-dependent signal transduction pathways, involved in the DNA damage response, response to stress, and regulation of apoptotic processes. In addition, differentially expressed genes, analyzed using the KEGG pathway da-tabase, were enriched for genes involved in platinum drug resistance and the p53 signaling pathway and gene ontology database for protein-protein interaction using an adjusted p-values less than 0.01.

2021-07-09 | GSE179787 | GEO

Mutant GNAS drives pancreatic tumorigenesis by inducing PKA-mediated SIK suppression and reprogramming lipid metabolism

Project description:Gain-of-function mutation of GNAS is frequently observed in pancreatic and other gastrointestinal cancers. Aim of this study was to delineate the oncogenic mechanisms downstream of mutant GNAS. To gain insight into the expression of different transcripts, we perforemd RNA-sequencing (RNA-seq) studies. For this total RNA was isolated in duplicate from two independent KRAS,GNAS mutant lines (A and B) grown in 3D culture with doxycycline supplementation. RNA samples were prepared using Illumina TruSeq RNA Sample Preparation Kit v2 and sequenced using an Illumina HiSeq 2500 sequencer, generating 50-bp single-end reads. Data was processed using a standard RNA-seq pipeline that used Tophat2 to align the reads to mm9, and the Cufflinks suite to calculate FPKM values. Our results shows level of RNA expression in KRAS, GNAS mutant cells.

2018-05-29 | GSE114348 | GEO

Rbfox2-coordinated alternative splicing of Mef2d and Rock2 controls myoblast fusion during myogenesis

2014-07-15 | E-GEOD-58928 | biostudies-arrayexpress

RNA-seq of mouse splenocytes following infection with Listeria monocytogenes

Project description:C57BL/6 mice were maintained in specific pathogen-free conditions in accordance with the Institutional Animal Care and Use Committee. Listeria monocytogenes (clinical isolate 10403s) was from V. Boyartchuk (NTNU, Trondheim, Norway) and infected as previously described (PMID 24477914) for 24 hours before harvesting the spleen. PBS was used as control. . Single cell suspensions from spleens were used to make total RNA. 4 Î¼g of total RNA was used to generate libraries for RNA-sequencing (Illumina unstranded mRNA kits). Samples were sized, quantified and validated on a Bioanalyzer. Libraries were sequenced on a High-Seq System (Illumina 2000, San Diego, CA) as paired-end 100 reads. Sequence reads were aligned to the mouse genome (assembly NCBI m37/mm9) using TopHat.Gene level read counts based on the resulting alignments were calculated using HTSeq. The DESeq R package was used to normalize gene counts, calculate fold change in gene expression, estimate p-values and adjusted p-values for change in gene expression values, and to perform a variance stabilized transformation on read counts.

2015-06-30 | E-MTAB-3315 | biostudies-arrayexpress

NFAT5/TonEBP controls metabolic adaption and tone of pulmonary vascular smooth muscle cells in the hypoxic lung [SMC]

Project description:Hypoxia provokes adaptive responses of cells, which ensure their energy supply including the adjustment of the transcriptome to match their metabolism. In this context, we explored the transcriptional impact of nuclear factor of activated T-cells 5 (NFAT5) on the function of vascular smooth muscle cells (VSMC) in the hypoxic lung. Exposure to hypoxia induced a rapid nuclear translocation of NFAT5 in cultured murine VSMCs. SMC-specific ablation of Nfat5 (Nfat5(SMC-/-)) increases the systolic pressure in the right ventricle (RVSP) of the mouse heart and impairs its function upon exposure to hypoxia for 7 and 21 days. Analyses of the transcriptome of the lung revealed a robust increase in the expression genes attributed to mitochondrial respiration. Further analyses of hypoxia-exposed pulmonary artery VSMCs revealed that loss of Nfat5 stimulates the expression of multiple mitochondria-related genes encoding cytochrome oxidases while decreasing the expression of lactate dehydrogenase A (Ldha) and phosphofructokinase 3 (Pfkfb3). Both, inhibition of LDHA or PFKFB3 activity and loss of Nfat5 stimulated the mitochondrial production of reactive oxygen species (ROS) in hypoxic pulmonary artery VSMCs while scavenging of ROS normalized the RVSP values in hypoxia-exposed Nfat5(SMC-/-) mice. In summary, our findings suggest a crucial role for NFAT5 in adjusting the transcriptome of hypoxia-exposed pulmonary artery VSMCs to support an adequate glycolysis-centered metabolism. Loss of Nfat5 impairs this response thereby fueling the mitochondrial respiration and ROS production that amplifies the hypoxia-mediated constriction of pulmonary arteries.

2021-11-26 | GSE178463 | GEO

NFAT5/TonEBP controls metabolic adaption and tone of pulmonary vascular smooth muscle cells in the hypoxic lung [normoxia vs hypoxia]

2021-11-26 | GSE178462 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data