Transcriptional Profiling of Normal, Stenotic, and Regurgitant Human Aortic Valves
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ABSTRACT:
PROVIDER: PRJNA643226 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12118617_1.fastq.gz | Fastqsanger.gz | |||
| SRR12118617_2.fastq.gz | Fastqsanger.gz | |||
| SRR12118618_1.fastq.gz | Fastqsanger.gz | |||
| SRR12118618_2.fastq.gz | Fastqsanger.gz | |||
| SRR12118619_1.fastq.gz | Fastqsanger.gz |
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Project description:We explored gene expression profile of human aortic valves in patients with or without aortic stenosis. The dataset that we generated constitutes a large-scale quantitative measurements of gene expression in normal and stenotic human valves. The goal was to compare gene expression levels between the two groups and identified a list of genes that are up- or down-regulated in aortic stenosis. Keywords: disease state analysis Gene expression was performed on ten normal and ten aortic stenosis valves
2009-07-09 | E-GEOD-12644 | biostudies-arrayexpress
Project description:We explored gene expression profile of human aortic valves in patients with or without aortic stenosis. The dataset that we generated constitutes a large-scale quantitative measurements of gene expression in normal and stenotic human valves. The goal was to compare gene expression levels between the two groups and identified a list of genes that are up- or down-regulated in aortic stenosis. Keywords: disease state analysis
2009-07-09 | GSE12644 | GEO
Project description:Purpose: The goal of this study is to characterize the gene expression profiles and identify genes of interest (GOI) in stenotic (AS) and regurgitant (AI) human aortic valves using RNA sequencing technology. Methods: Aortic valve leaflets were collected from non-matched transplant donor hearts (NC) and from aortic valve replacement operations (AS or AI). Leaflets were washed in cold PBS, snap frozen, and stored at -80°C until RNA extraction. For each sample, total RNA was extracted, a cDNA library was generated, and an Illumina HiSeq 2500 was used to sequence each sample. 150-bp paired-ends reads were generated with approximately 30 million reads per sample. Sequencing was performed in duplicate for each sample. Paired-end clean reads were aligned to the reference genome using STAR. HTSeq v0.6.1 was used to count the read numbers mapped to each gene. Fragments per kilobases per million (FPKM) were calculated to estimate the level of gene expression. Differential gene expression analysis was performed using the DESeq2 R package. A model based on the negative binomial distribution was used to determine differential gene expression (log 2 [fold change]). P-values were adjusted using the Benjamini-Hochberg approach for controlling false discovery rate. Genes with adjusted P-values <0.05 found by DESeq2 were assigned as differentially-expressed genes (DEGs). Results: RNA sequencing analysis identified 8,621 DEGs among the AS, AI, and NC valves. Specifically, 6,438 genes were found to be differentially expressed between the AS and NC groups; 4,994 genes were found to be differentially expressed between the AI and NC groups; and 2,771 genes were found to be differentially expressed between the AS and AI groups. Of these, 1,979 (31%) genes were expressed at statistically different levels between the AS and NC groups, and 1,428 (29%) genes were expressed at statistically different levels between the AI and NC groups Conclusions: Overall, we have sequenced aortic leaflets from non-matched transplant donor hearts , thereby establishing a new standard for normal aortic valve transcriptional profiling. We have also sequenced human aortic valves with AI and demonstrated that valves with AI and AS possess unique gene expression patterns. The plethora of DEGs identified and our novel PPI networks for AS and AI have revealed new pathways missed by previous aortic valve studies. Our data lays the foundation for future gene editing studies and may guide researchers in developing novel therapeutic interventions for AS and AI.
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Project description:The objective of this study was to identify genes differentially expressed between calcified bicuspid aortic valves (BAV) and tricuspid valves with (TAVc) and without (TAVn) aortic valve stenosis. Ten human BAV and nine TAVc were collected from male who underwent primary aortic valve replacement. Eight TAVn were obtained from male who underwent heart transplantation. mRNA levels were measured using Illumina HumanHT-12 v4 Expression BeadChip and compared between valve groups.
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Project description:Aortic valves are collected from patients with severe aortic valve stenosis undergoing aortic valve replacement at the Institut universitaire de cardiologie et de pneumologie de Québec (IUCPQ), Quebec City, Canada. From this biobank collection, transcriptomic analyses from 240 aortic valves were performed. All valves were tricuspid and had a fibro-calcific remodeling score of 3 or 4. RNA was extracted from valve leaflets and gene expression evaluated using the Illumina HumanHT-12 v4 Expression BeadChip. The main objective of this study was to perform a large-scale expression quantitative trait loci (eQTL) mapping study on human aortic valves.
2018-03-14 | GSE102249 | GEO
Project description:We are looking at differential gene/protein expression in tissue from stenotic vs sclerotic human aortic valves in order to identify genetic predispositions for aortic valve disease, as well as novel targets for diagnostic and therapeutic strategies.
2019-10-08 | GSE138531 | GEO
Project description:Purpose: The molecular mechanisms leading to premature development of aortic valve stenosis (AS) in individuals with a bicuspid aortic valve (BAV) are unknown. The objective of this study was to identify genes differentially expressed between calcified BAV and tricuspid valves with (TAVc) and without (TAVn) calcification using RNA sequencing (RNA-Seq). Methods: Ten human calcified BAV and nine TAVc were collected from men who underwent aortic valve replacement. Eight TAVn were obtained from men who underwent heart transplantation. mRNA levels were measured using the Illumina HiSeq 2000 system. Reads were aligned with TopHat. Cuffdiff, DESeq, edgeR, and SAMSeq were used to compare gene expression. Genes with adjusted P < 0.05 in the four methods were called differentially expressed. Pathway analysis was performed with IPA. Results: Two genes were up-regulated and none were down-regulated in BAV compared to TAVc. There were 462 genes up-regulated and 282 down-regulated in BAV compared to TAVn. Compared to TAVn, 329 genes were up- and 170 were down-regulated in TAVc. Conclusions: This is the first transcriptome study on calcified and normal aortic valves using RNA-Seq. BAV and TAVc have a highly similar expression profile. These results contribute to our molecular understanding of AS and the identification of new therapeutic targets that are urgently needed to prevent, slow the development or treat AS in patients with bicuspid and tricuspid valves.
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