Identification of candidate genes involved in coronary artery calcification by transcriptome sequencing of cell lines

Abstract

Background: Massively-parallel cDNA sequencing (RNA-Seq) is a new technique that holds great promise for cardiovascular genomics. Here, we used RNA-Seq to study the transcriptomes of matched coronary artery disease cases and controls in the ClinSeq® study, using cell lines as tissue surrogates.

Results: Lymphoblastoid cell lines (LCLs) from 16 cases and controls representing phenotypic extremes for coronary calcification were cultured and analyzed using RNA-Seq. All cell lines were then independently re-cultured and along with another set of 16 independent cases and controls, were profiled with Affymetrix microarrays to perform a technical validation of the RNA-Seq results. Statistically significant changes (p < 0.05) were detected in 186 transcripts, many of which are expressed at extremely low levels (5-10 copies/cell), which we confirmed through a separate spike-in control RNA-Seq experiment. Next, by fitting a linear model to exon-level RNA-Seq read counts, we detected signals of alternative splicing in 18 transcripts. Finally, we used the RNA-Seq data to identify differential expression (p < 0.0001) in eight previously unannotated regions that may represent novel transcripts. Overall, differentially expressed genes showed strong enrichment (p = 0.0002) for prior association with cardiovascular disease. At the network level, we found evidence for perturbation in pathways involving both cardiovascular system development and function as well as lipid metabolism.

Conclusions: We present a pilot study for transcriptome involvement in coronary artery calcification and demonstrate how RNA-Seq analyses using LCLs as a tissue surrogate may yield fruitful results in a clinical sequencing project. In addition to canonical gene expression, we present candidate variants from alternative splicing and novel transcript detection, which have been unexplored in the context of this disease.

Figure 1

Volcano plots from edgeR (panel A) and ANOVA (panel B) analyses of RNA-Seq count data. X-axis on both panels shows base 10 logarithm of fold change (case/control). Y axis shows p value. Red dots indicate 186 transcripts meeting p < 0.05 in both tests.

Figure 2

Plot showing RPKM values of 186 differentially expressed transcripts and their absolute expression (measured as copies/cell). Log10 RPKM values for controls and cases are shown on the x and y-axes, respectively. The dashed diagonal line represents equal RPKM values for both cases and controls, and hence no differential expression. Thus, the orthogonal distance to the dashed line of a point indicates the amount of differential expression. The blue numbers on the dashed line indicate the log10 measure of copies/cell for the seven spike-in RNA controls (ranging from 1–100000), and the position along the x and y-axes indicate the corresponding RPKM values recorded for these spikes. Based on a linear regression model for copies/cell as a function of RPKM, predictions for absolute expression measured as copies/cell for the 186 transcripts were made from these spike-in values. For each position, the average of the RPKMs corresponding to the x and y-coordinate values yield predictions of cell copies based on this model and a corresponding color shown in the background of the plot ranging from green (low) to red (high).

Figure 3

Validation of RNA-Seq results with microarray data. (A) Fold changes (case/control) from first 16 subjects, measured by RNA-Seq (Y-axis) and microarrays (X-axis) in logarithm base 10 scale. Red dots show 110 transcripts (out of 186 in Figure  1) that were upregulated or downregulated in both experiments. (B) Comparison of microarray data from first and second groups of 16 subjects (X- and Y-axes, respectively). Red dots show 71 transcripts out of 110 in panel A that were upregulated or downregulated in both groups. Contingency tables for statistical calculations are shown below each panel.

Figure 4

Immunoblot demonstrating the expression of IGLL5 in carotid plaques obtained at endarterectomy. Lanes 1–3: lysates from an area exhibiting diffuse intimal thickening (DIT). Lanes 4–6: matched lysates of fibroatheromatous plaque from the same samples. Panel A: anti-human IGLL5 antibody staining. Panel B: Beta-actin band used for densitometric quantification of IGLL5. Panel C: Increased trends of IGLL5 expression in fibroatheromatous plaques. See also Additional file 1: Figure S3 for siRNA knockdown experiments of to establish specificity of this antibody for IGLL5.

Figure 5

Putative novel transcript on chromosome 12 detected by Cufflinks. UCSC Genome Browser tracks in vertical order: (A) Chromosomal location. (B) WIG file showing expression levels. (C) BAM file showing mapped RNA-Seq reads (blue bars for forward strand; red for reverse). Segments joined by thin lines represent reads spanning a putative splice junction. (D) Assorted gene and gene prediction tracks showing absence of prior annotations for the novel transcript. (E) ENCODE enhancer- and promoter-associated histone mark H3K4Me1.

Figure 6

Examples of alternative splicing detected using exon-level analysis of RNA-Seq counts in PIGQ (top panel) and VIM (bottom panel). X axis shows genomic location of exons (red and black dots for cases and controls, respectively) in hg18 coordinates. Y axis shows mean-subtracted normalized RNA-Seq read counts for each exon. Arrows at bottom right show direction of transcription. Neither gene was differentially expressed when all exons were considered together.