Abstract
BackgroundMassively-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.ResultsLymphoblastoid 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.ConclusionsWe 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.
Highlights
Massively-parallel cDNA sequencing (RNA-Seq) is a new technique that holds great promise for cardiovascular genomics
Assessment of Coronary Artery Disease (CAD) burden and RNA-Seq experimental design To quantify CAD status, study subjects were assessed at enrolment for CAC, using multi-slice computed tomography followed by Agatston scoring
It is intriguing that we detected differential expression of four genes involved in cAMP metabolism (ADM, APLP1, PRKCA and PTHLH), as existing studies focusing on individual genes involved in arterial calcification collectively suggest that perturbation of ATP metabolism plays a role in this process [34]. In summary, in this project we piloted the study of coronary artery calcification using cell lines as a patient surrogate for gene expression
Summary
Massively-parallel cDNA sequencing (RNA-Seq) is a new technique that holds great promise for cardiovascular genomics. 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. Current knowledge of the CAD transcriptome is based on gene expression microarrays, a technology that is useful but has several limitations. RNA-Seq, a powerful new technique for transcriptome analysis has revolutionized gene expression analyses by providing the ability to simultaneously interrogate all transcripts in an RNA sample (unlike microarrays which are limited to previously annotated transcripts) [6]. We present results from a pilot application of RNA-Seq on human cases and controls chosen to reflect extremes for coronary artery calcification (CAC), a clinical marker for advanced CAD that is highly correlated with future adverse cardiovascular events [7]. As a patient surrogate for gene expression, we used EpsteinBarr virus transformed lymphoblastoid cell lines (LCLs), which have been shown in multiple studies to reliably reflect gene expression signatures [8], those associated with nearby cis-acting genomic polymorphisms (expression quantitative trait loci or eQTLs) [9]
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