Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and miR-21-/- Smooth Muscle Cell Transcriptomes
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ABSTRACT:
PROVIDER: PRJNA762066 | ENA |
REPOSITORIES: ENA
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Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to investigate the effect of miR-21 knockout in aortic smooth muscle cell transcriptome profiling (RNA-seq) to understand how miR-21 regulates SMC function at basal level. Methods: Aortic smooth muscle cell (SMC) mRNA profiles of 12-week-old wild-type (WT) and SMC specific miR-21 knockout (miR-21fl/flmTmG/Myh11ERT2) mice were generated by deep sequencing, in triplicate. The sequence reads that passed quality filters were analyzed at the transcript isoform level . qRT–PCR validation was performed using TaqMan and SYBR Green assays to confirm miR-21 knockout. Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts in the SMCs of WT and miR-21fl/flmTmG/Myh11ERT2 mice. RNA-seq data revealed >400 genes differentially expressed in miR-21 knockout SMCs. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized pathways that may contribute to SMC function. Conclusions: Our study represents the first detailed analysis of SMC lacking of miR-21 transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
2022-06-10 | GSE183830 | GEO
Project description:Purpose: The goals of this study are to investigate the effect of miR-21 knockout in aortic smooth muscle cell transcriptome profiling (RNA-seq) to understand how miR-21 regulates SMC plasticity during atherosclerosis. Methods: Aortic smooth muscle cell (SMC) mRNA profiles of 30-week-old wild-type (WT) and SMC specific miR-21 knockout (miR-21fl/flmTmG/Myh11ERT2) mice fed on western diet for 20 weeks were generated by deep sequencing, in triplicate. The sequence reads that passed quality filters were analyzed at the transcript isoform level . qRT–PCR validation was performed using TaqMan and SYBR Green assays to confirm miR-21 knockout. Results: Using an optimized data analysis workflow, we mapped about 60 million sequence reads per sample to the mouse genome (build mm10) and identified 22,836 transcripts in the SMCs of WT and miR-21fl/flmTmG/Myh11ERT2 mice. RNA-seq data revealed ~300 genes differentially expressed in miR-21 knockout SMCs after atherosclerosis. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized pathways that may contribute to SMC function during atherosclerosis progression. Conclusions: Our study represents the first detailed analysis of SMC lacking of miR-21 transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
2022-06-10 | GSE183829 | GEO
Project description:Analysis of Wild Type and miR-21-/- Smooth Muscle Cell Transcriptomes after atherosclerosis
| PRJNA762067 | ENA
Project description:Overexpression and inhibition of miR-29 (pre-miR and anti-miR to miR-29b) in murine aortic smooth muscle cells, analysis of their secretome (conditioned media after serum starvation), n=3 for all four groups (pre-miR control, pre-miR-29b, anti-miR control, anti-miR-29b).
2016-05-06 | PXD003938 | Pride
Project description:Purpose: Methyltransferase SMYD2 has been implicated in cancer cell growth and cardiomyocytes development. However, its role in vascular smooth muscle cell(VSMC) is still unknown. This study aims to investigate differentially expressed genes in Flox and SMYD2 knockout VSMC. Methods:RNA profiles of Aortic Smooth Muscle Cell isolated from Flox and SMYD2 knockout (SMYD2−/−) mice were generated by deep sequencing, in triplicate, using BGISEQ-500 platform. Firstly, we filter the low quality reads (More than 20% of the bases qualities are lower than 10),reads with adaptors and reads with unknown bases (N bases more than 5%)to get the clean reads. Then we map those clean reads onto reference genome, followed with novel gene prediction, SNP &INDEL calling and gene splicing detection. Finally, we identify DEGs (differentially expressed genes) between samples and do clustering analysis and functional annotations. Results: In our project, we sequenced 6 samples on BGISEQ-500 Platform in total and generated about 4.69 Gb per sample. The average genome mapping rate is 96.62% and the average gene mapping rate is 80.92%. 18,112 genes were identified in which 16,992 of them are known genes and 1,351 of them are novel genes. 13,680 novel transcipts were identified in which 11,251 of them are previously unknown splicing event for known genes, 1,351 of them are novel coding transcripts without any known features, and the remaining 1,078 are long noncoding RNA. Conclusions: Our study represents the first detailed analysis of SMYD2 KO VSMC transcriptomes, with biologic replicates, generated by RNA-seq technology.
2023-05-12 | GSE161004 | GEO
Project description:Purpose: Purpose: we used next generation sequencing to analyze gene expression profiles of pancreatic tissues from wild-type (WT) and miR-21-/- (miR-21 KO) mice treated with saline(control) or caerulein. The goals of this study are to compare the different gene expression profiles of pancreatic tissue between WT and miR-21 KO mice treated with caerulein. Methods: Female WT and miR-21 KO mice were administered 8-hourly intraperitoneal injection of 50μg/kg caerulein. Mice were sacrificed at 24 hours after the first caerulein injection and total RNA was extracted. mRNArna profiles were generated by deep sequencing WT treated with saline in single, WT treated with caerulein in duplicate, miR-21KO treated with caerulein in duplicate, using High-seq 2000 Illumina sequencing platform. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: After quality filtering of raw sequencing data, we obtained 32,797,327 (69.9%) out of 46,941,672 tags from WT mice treated with saline, 42,587,312 (87.1%) out of 48,919,213 tags from WT mice treated with caerulein, and 57,139,408 (85.1%) out of 67,111,415 tags from miR-21 KO mice treated with caerulein. We then mapped these tags to the mouse genome. We identified 1457 differential expressed genes (DEGs) between WT mice treated with caerulein and with saline, and 152 genes between WT mice and miR-21 KO mice treated with caerulein with a fold change more than 2.0 (up-regulation) or less than 0.5 (down-regulation). Altered expression of 16 genes was confirmed with qRT-PCR. Conclusions: Our study represents the first detailed analysis of pancreatic transcriptomes generated by RNA-seq technology. By using RNA-seq based transcriptome analysis, we identified 6 miR-21 target genes and 10 downregulated genes involved in pancreatic injury. Specifically, up-regulation of Pias3 and down-regulation of Hmgb1 when miR-21 was ablated coincided with reduced pancreatitis severity. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex pancreatic functions.
2017-11-07 | GSE106550 | GEO
Project description:Thiele2013 - Smooth muscle smooth muscle cells
The model of smooth muscle smooth muscle cells metabolism is derived from the community-driven global reconstruction of human metabolism (version 2.02, MODEL1109130000
).
This model is described in the article:
A community-driven global reconstruction of human metabolism.
Thiele I, et al
.
Nature Biotechnology
Abstract:
Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven,
consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared
with its predecessors, the reconstruction has improved topological and functional features, including ~2x more reactions and ~1.7x more unique metabolites. Using
Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic
data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically
generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will
facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
This model is hosted on BioModels Database
and identified by: MODEL1310110025
.
To cite BioModels Database, please use: BioModels Database: An enhanced,
curated and annotated resource for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer
to CC0 Public Domain Dedication
for more information.
2005-01-01 | MODEL1310110025 | BioModels
Project description:We extracted RNA from primary smooth muscle cells from five KitW/WV mice and five Wild type mice and used next generation RNA sequenceing to compare the difference in gene expression profiles
2024-03-02 | GSE168110 | GEO
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived retinal transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis
2022-09-20 | GSE166816 | GEO
Project description: Not available
| S-EPMC6555864 | biostudies-literature