Transcriptional Profiling of Normal, Stenotic, and Regurgitant Human Aortic Valves
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. Overall design: Discovery of differentially expressed genes when comparing Normal, Stenotic, and Regurgitant Human Aortic Valves using RNA-seq technology.