Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Liver-specific overexpression of GBP5 induces liver injury and inflammation

ABSTRACT: Purpose: The goal of this study is to compare transcriptome profilings of livers in which GBP5 and βGal were overexpressed, respectively. Methods: Liver-specific GBP5-overexpressing and βGal–overexpressing mice were generated by tail-vein injection of individual purified adenovirus(6 × 1011 viral particles per mouse), respectively. RNA-seq was performed by using HiSeq X Ten platform in a single test. Paired-end clean reads were aligned to the mouse reference genome (Ensemble_GRCm38.89) with TopHat (version 2.0.12), and the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1). Conclusion: Our study represents the first detailed analysis of liver transcriptomes from GBP5- and βGal-overexpressing livers, generated by RNA-seq technology. Our results show that 633 genes were significantly up-regulated in the GBP5-overexpressing livers, whereas 586 genes were dramatically down-regulated in the GBP5-overexpressing livers. GO analysis showed that some up-regulated genes were related to the immune system process, response to stress, cell cycle, and cell death in GBP5-overexpressing livers.

ORGANISM(S): Mus musculus

PROVIDER: GSE124520 | GEO | 2021/12/29

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

D-galactosamine (GalN) and lipopolysaccharide (LPS) effect on GBP5 deficient liver

Project description:Purpose: The goal of this study is to compare transcriptome profilings of liver from GBP5 knockout and WT control mice treated with GalN/LPS. Methods: GBP5 knockout and WT control mice were treated with GalN (800 μg/g body weight) and LPS (100 ng/g body weight) for 6 h to induce liver inflammation and injury. RNA samples were pooled from livers of GBP5 KO and WT mice (n=4 for each group). RNA-seq was performed by using HiSeq X Ten platform. Paired-end clean reads were aligned to the mouse reference genome (Ensembl_GRCm38.89) with TopHat (version 2.0.12), and the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1). Conclusion: Our study represents the first detailed analysis of liver transcriptomes from GBP5 KO and WT mice treated with GalN/LPS, generated by RNA-seq technology. The RNA-seq analysis showed that 405 genes were down-regulated and 33 genes were up-regulated in the liver of GBP5 KO mice. GO analysis showed that the down-regulated genes were primarily related to the immune system process and response to stress. KEGG pathway enrichment analysis showed that phagocytosis and Jak-STAT signaling pathway were significantly decreased in the liver of GBP5 KO mice.

2021-12-28 | GSE124473 | GEO

RNA-seq Analysis of mRNA profiles in Purβ-overexpressing and Purβ-knocking down hepatocytes

Project description:Purpose: The goal of this study is to investigate how Purβ regulates hepatic glucose production in primary hepatocytes. Methods: mRNA profiles of Purβ-overexpressing, and Purβ-knocking down hepatocytes were generated by deep sequencing using an Illumina NovaSeq 6000 platform. Paired-end clean reads were aligned to the mouse reference genome(Ensemble_GRCm38.96) with TopHat (version 2.0.12), and the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1). Conclusion: Our study represents the first detailed analysis of mRNA profiles in Purβ-overexpressing, and Purβ-knocking down hepatocytes, generated by RNA-seq technology. Our results identified Adcy6 was a target of Purβ. RNA-seq analysis showed that Purβ overexpression increased Adcy6 expression, whereas Purβ knockdown decreased Adcy6 expression.

2019-09-23 | GSE136728 | GEO

RNA-seq Analysis of mRNA profiles in β-Gal-overexpressing, and HuR-overexpressing primary hepatocytes

Project description:Purpose: The goal of this study is to investigate how HuR regulates hepatocyte death. Methods: mRNA profiles of β-Gal-overexpressing, and HuR-overexpressing primary hepatocytes were generated by deep sequencing using an Illumina HiseqX platform. Paired-end clean reads were aligned to the mouse reference genome(Ensemble_GRCm38.83) with TopHat (version 2.0.12), and the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1). Conclusion: Our study represents the first detailed analysis of mRNA profiles in HuR-overexpressing primary hepatocytes, generated by RNA-seq technology. Our results show that 258 genes were upregulated and 357 genes were downregulated following HuR overexpression. GO analysis indicated that the upregulated genes were primarily related to cell death, death, apoptotic process, programmed cell death and intrinsic apoptotic signaling pathway. These data indicate that upregulation of HuR leads to hepatocyte death.

2023-06-15 | GSE152542 | GEO

RNA-seq analysis of mRNA profiles in β-Gal-overexpressing, NIK-overexpressing, and NIK(KA)-overexpressing INS-1 832/13 cells

Project description:Purpose: The goal of this study is to investigate how NIK regulates islet β-cell function. Methods: mRNA profiles of β-Gal-overexpressing, NIK-overexpressing, and NIK(KA)-overexpressing INS-1 832/13 cells were generated by deep sequencing using an Illumina Hiseq platform. Paired-end clean reads were aligned to the rat reference genome (ftp://ftp.ensembl.org/pub/release-90/fasta/rattus_norvegicus/dna/) with TopHat (version 2.0.12), and the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1). Conclusion: Our study represents the first detailed analysis of mRNA profiles in NIK-overexpressing and NIK(KA)-overexpressing INS-1 832/13 cells, generated by RNA-seq technology. Our results show that 471 genes were upregulated and 249 genes were downregulated following NIK overexpression. GO analysis indicated that the upregulated genes were primarily related to the immune response. KEGG pathway enrichment analysis showed that immune signaling pathways, including the TNF, NF-κB, antigen processing and presentation signaling pathways, were significantly activated by NIK overexpression in INS-1 832/13 cells, whereas genes related to insulin secretion and regulation of secretion were significantly decreased. NIK(KA), the dominant-negative mutant of NIK, does not induce the expression of genes related to immune response in INS-1 832/13 cells.

2020-07-28 | GSE130121 | GEO

The effects of WTAP on BAT function

Project description:Purpose: The goal of this study is to compare transcriptome profilings of interscapular brown adipose tissue (iBAT) from BAT-specific WTAP knockout and control mice Methods: iBATs were collected from WTAP BKO and f/f mice at 8 weeks old (n=3 for each group), and total RNA was isolated using TriPure. RNA-seq was performed by using Illumina NovaSeq 6000 platform. Paired-end clean reads were aligned to the mouse reference genome (Ensemble_grcm38_p6) with TopHat (version 2.0.12), and the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1). Conclusion:Our study represents the first detailed analysis of iBAT transcriptomes from WTAP BKO and f/f mice, generated by RNA-seq technology. The RNA-seq analysis showed that 894 genes were down-regulated and 1410 genes were up-regulated in the iBAT of WTAP BKO mice. GO analysis showed that the down-regulated genes were primarily related to related to generation of precursor metabolites and energy, cellular respiration, energy derivation by oxidation of organic compounds, electron transport chain, nucleotide metabolic process and purine ribonucleotide metabolic process.

2022-10-19 | GSE202510 | GEO

RNA-seq analysis of mRNA profiles in the livers of Wtapflox/flox and hepatocyte-specific Wtap knockout (HKO) mice

Project description:Purpose: The goal of this study is to compare transcriptome profilings of liver from Wtapflox/flox and hepatocyte-specific Wtap knockout (HKO) mice. Methods: Total RNA was extracted using Tripure Isolation Reagent (Roche, Mannheim, Germany) from livers of Wtap flox/flox and Wtap-HKO mice at 8 weeks old. mRNA profiles were generated by deep sequencing using an Illumina Novaseq platform. Paired-end clean reads were aligned to the mouse reference genome(Ensemble_GRCm38.p6) with TopHat (version 2.0.12), and the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1). Conclusion: Our study represents the first detailed analysis of hepatic mRNA profiles in Wtapflox/flox and Wtap-HKO mice at 8 weeks old, generated by RNA-seq technology. Our results showed that 3486 genes were downregulated, and 3706 genes were upregulated. Gene ontology (GO) analysis showed that downregulated genes were associated with small molecule catabolic process, fatty acid metabolic process, amino acid metabolic process, steroid metabolic process, cholesterol metabolic process, xenobiotic metabolic process, fatty acid beta-oxidation, alcohol metabolic process, and glucose metabolic process, whereas the upregulated genes were associated with wound healing, leukocyte migration, chemotaxis, and positive regulation of cytokine production.

2022-06-02 | GSE168850 | GEO

RNA-seq Analysis of Transcriptomes in β-Gal-overexpressing, NIK-overexpressing, and NIK(KA)-overexpressing αTC1-6 cells

Project description:Purpose: The goal of this study is to determine the molecular mechanisms involved in the NIK regulation of α cell function. Methods: mRNA profiles of β-Gal control, NIK, and NIK(KA) overexpressed αTC1-6 cells were generated by deep sequencing using an Illumina Hiseq platform. Paired-end clean reads were aligned to the mouse reference genome(Ensemble_GRCm38.89) with TopHat (version 2.0.12), and the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1). Conclusion: Our study represents the first detailed analysis of NIK-overexpressing aTC1-6 cell transcriptomes, generated by RNA-seq technology. Our results show that 328 genes were upregulated and 48 genes were downregulated following NIK overexpression. GO analysis indicated that the upregulated genes were primarily related to the immune response. KEGG pathway enrichment analysis showed that immune signaling pathways, including the TNF, NF-κB, and chemokine signaling pathways, were significantly activated by NIK overexpression in aTC1-6 cells, while genes related to metabolism-associated signal pathways were significantly decreased. NIK(KA), the dominant-negative mutant of NIK, does not affect gene expression in aTC1-6 cells.

2018-12-30 | GSE112511 | GEO

The effects of METTL3 on BAT development and thermogenesis

Project description:Purpose: The goal of this study is to compare transcriptome profilings of interscapular brown adipose tissue (iBAT) from BAT-specific METTL3 knockout and control mice Methods: iBATs were collected from METTL3 BKO and f/f mice at 8 weeks old, and total RNA was isolated using TriPure. RNA samples were pooled from iBATs of METTL3 BKO and f/f mice (n=4 for each group). RNA-seq was performed by using Illumina NovaSeq 6000 platform. Paired-end clean reads were aligned to the mouse reference genome (Ensemble_GRCm38.90) with TopHat (version 2.0.12), and the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1). Conclusion:Our study represents the first detailed analysis of iBAT transcriptomes from METTL3 BKO and f/f mice, generated by RNA-seq technology. The RNA-seq analysis showed that 530 genes were down-regulated and 1552 genes were up-regulated in the iBAT of METTL3 BKO mice. GO analysis showed that the down-regulated genes were primarily related to developmental maturation, respiratory electron transport chain, adaptive thermogenesis, and energy deprivation by oxidation of organic compounds whereas the up-regulated genes were associated with muscle system process and muscle cell development.

2020-02-06 | GSE133216 | GEO

RNA-seq analysis of mRNA profiles in the livers of Mettl3 flox/flox and hepatocyte-specific Mettl3 knockout (HKO) mice

Project description:Purpose: The goal of this study is to compare transcriptome profilings of liver from Mettl3 flox/flox and hepatocyte-specific Mettl3 knockout (HKO) mice. Methods: Total RNA was extracted using Tripure Isolation Reagent (Roche, Mannheim, Germany) from livers of Mettl3 flox/flox and HKO mice at 8 weeks old. mRNA profiles were generated by deep sequencing using an Illumina HiSeq X Ten platform. Paired-end clean reads were aligned to the mouse reference genome(Ensemble_GRCm38.90) with Hisat2 (version 2.0.4), and the aligned reads were used to quantify mRNA expression by using HTSeq (version 0.9.1). Conclusion: The hepatic mRNA profiles in Mettl3 flox/flox and HKO mice were characterized.

2021-10-29 | GSE141216 | GEO

Title: RNA-seq analysis of mRNA profiles in the livers of HuR flox/flox and hepatocyte-specific HuR knockout (HKO) mice fed with an MCD for 3 weeks.

Project description:ionPurpose: The goal of this study is to investigate how HuR regulates NASH progression. Methods: The hepatic mRNA profiles of HuR flox/flox and hepatocyte-specific HuR knockout (HKO) mice (n=3 for each group) fed with an MCD for 3 weeks were generated by deep sequencing using an Illumina Novaseq platform. Paired-end clean reads were aligned to the mouse reference genome(Ensemble_GRCm38.p6) with TopHat (version 2.0.12), and the aligned reads were used to quantify mRNA expression by using HTSeq-count (version 0.6.1). Conclusion: Our study represents the first detailed analysis of hepatic mRNA profiles in HuR flox/flox and hepatocyte-specific HuR knockout (HKO) mice fed with an MCD for 3 weeks, generated by RNA-seq technology. Our results show that 353 genes were upregulated and 422 genes were downregulated. Gene Ontology (GO) analysis showed that genes related to leukocyte migration, leukocyte chemotaxis, cell chemotaxis, neutrophil migration and Fc receptor signaling pathway were significantly decreased, which contributes to the protective effects of NASH progression in HKO mice.

2023-06-17 | GSE152670 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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