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. 2011;17(11-12):1365-73.
doi: 10.2119/molmed.2011.00286. Epub 2011 Sep 27.

Unraveling divergent gene expression profiles in bicuspid and tricuspid aortic valve patients with thoracic aortic dilatation: the ASAP study

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Unraveling divergent gene expression profiles in bicuspid and tricuspid aortic valve patients with thoracic aortic dilatation: the ASAP study

Lasse Folkersen et al. Mol Med. 2011.

Abstract

Thoracic aortic aneurysm (TAA) is a common complication in patients with a bicuspid aortic valve (BAV), the most frequent congenital heart disorder. For unknown reasons TAA occurs at a younger age, with a higher frequency in BAV patients than in patients with a tricuspid aortic valve (TAV), resulting in an increased risk for aortic dissection and rupture. To investigate the increased TAA incidence in BAV patients, we obtained tissue biopsy samples from nondilated and dilated aortas of 131 BAV and TAV patients. Global gene expression profiles were analyzed from controls and from aortic intima-media and adventitia of patients (in total 345 samples). Of the genes found to be differentially expressed with dilation, only a few (<4%) were differentially expressed in both BAV and TAV patients. With the use of gene set enrichment analysis, the cell adhesion and extracellular region gene ontology sets were identified as common features of TAA in both BAV and TAV patients. Immune response genes were observed to be particularly overexpressed in the aortic media of dilated TAV samples. The divergent gene expression profiles indicate that there are fundamental differences in TAA etiology in BAV and TAV patients. Immune response activation solely in the aortic media of TAV patients suggests that inflammation is involved in TAA formation in TAV but not in BAV patients. Conversely, genes were identified that were only differentially expressed with dilation in BAV patients. The result has bearing on future clinical studies in which separate analysis of BAV and TAV patients is recommended.

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Figures

Figure 1
Figure 1
Principal component analysis based on gene expression of all 11,611 expressed genes in all samples. The purpose of this analysis is to give an overview of the data distribution in general and to specifically evaluate the use of the different control groups. Not surprisingly, the analysis shows separation mostly between different tissue types. Importantly, the transplant control samples roughly cluster in the middle, indicating their general similarity with nondilated intima-media control samples. Within tissue types the different patient groups are scattered randomly, indicating that regulation of only a smaller amount of genes reflect the difference between patient groups.
Figure 2
Figure 2
(A) The number of differentially expressed genes in different subgroups of patients. Blue background indicates a TAV to BAV comparison. Yellow background indicates a dilated to nondilated comparison. Grey text indicates a tunica change. Differential expression was defined as a difference in expression level at a FDR 5%, corresponding to P < 0.00077 for all expressed genes. (B) Distribution of the raw P values and the false discovery rate cutoff. Each plot shows different tissue and different blue and yellow comparisons as indicated in (A). Each axis of a plot compares a different subset of patients. A complete overview of the differentially expressed genes can be found in Supplementary S4 and S5.
Figure 3
Figure 3
The percentage of differentially expressed genes on comparison of different subgroups of patients and different subsets of genes. Differential expression is defined as a difference in expression level at a false discovery rate of 5%, and P values are as noted in the table. The top section compares all 11,611 expressed genes, and the two following sections show the extracellular region and immune response subsets, respectively. These gene sets were selected because of their identification using the GAGE algorithm (Supplementary S3). The effect of changing to similar gene sets (for example, from immune response to inflammatory response) is negligible. Lists of which genes are differentially expressed in each set and plots comparing distribution of the raw P values similar to the plots in Figure 2B are provided in Supplementary S4 and S5, respectively.
Figure 4
Figure 4
Mean expression of immune response genes by patient group and tissue type. Each grey circle shows the mean of the expression of 1 of the 85 expressed genes from the immune response gene set. Common cell type markers are highlighted in black filled circles: CD68, CD4, HLA-DMB, CD8 and CD3D. Error bars indicate standard error of the mean; *comparison significant at P < 0.05. All highlighted genes are significantly differentially expressed between transplant controls and aorta intima-media from dilated TAV patients. Supplementary material S6 shows other well-known immune response genes that follow the same pattern of expression: CD163, CD3E, CD302, CXCR4, HLA-DOA and IL1B, as well as immune response genes that do not change between these patient groups: IL13, IL17F, IL17A, IL9, IL4, IL5 and FOXP3. Likewise the plot for extracellular region genes is found here.
Figure 5
Figure 5
Differential immune response gene expression in dilated aortic intima-media. Comparison is made between dilated TAV (n = 23) and BAV (n = 47) patients. The y-axis indicates significance of difference and the x-axis indicates the magnitude and direction of change—negative values show overexpression in TAV patients and positive in BAV patients. Each dot is a gene, so a point in the upper left corner indicates a gene that is significantly overexpressed in the dilated aortic intima-media of TAV patients. A version of the figure with further labelling of gene names, as well as additional comparisons is found in Supplementary S7.
Figure 6
Figure 6
Immunohistochemical stainings showing CD4 in the aortic media of nondilated or dilated BAV or TAV patients. Images showing additional zoom levels as well as CD68 and CD163 stainings are available in Supplementary S8.

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