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

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.

Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts

“…The models we developed in this study aimed at predicting the binary case-control statuses of age-matched Caucasian male patients. Hence, we first transformed the CAC scores (measured by Agatston method [ 47 ]) of the 32 Caucasian male subjects from the ClinSeq®; study that formed our discovery cohort (data previously published in [ 42 , 43 ]) into binary CAC states. 16 control subjects in this cohort had zero CAC scores corresponding to state “0", whereas the 16 age-matched cases had high CAC scores (ranging between 500 and 4400) corresponding to state “1".…”

“…First, we leveraged a literature based strategy based on previous association studies of CAC to define a set of 57 single nucleotide polymorphisms (SNPs). As an alternative contextual approach, we utilized a standard feature selection and filtering approach in machine learning to identify 56 additional SNPs from the ClinSeq®; genotype data [ 42 , 43 ]. We assessed the predictive performances of these sets of SNPs with and without clinical variables in the ClinSeq®; cohort.…”

Machine learning identifies SNPs predictive of advanced coronary artery calcium in ClinSeq® and Framingham Heart Study cohorts

“…The models we developed in this study aimed at predicting the binary case-control statuses of Caucasian male patients. Hence, we first transformed the CAC scores (measured by Agatston method (Agatston et al, 1990)) of the 32 Caucasian male subjects from the ClinSeq® study that formed our discovery cohort (data previously published in (Sen et al, 2014a, b)) into binary CAC states. 16 control subjects in this cohort had zero CAC scores corresponding to state “0”, whereas the 16 age-matching cases had high CAC scores (ranging between 500 and 4400) corresponding to state “1”.…”

“…Our study focused on Caucasian males due to higher coronary calcium scores observed among men compared to women (Raggi et al, 2008; Maas and Appelman, 2010), as well as higher prevalence of coronary calciumamong white Americans compared to black Americans (Lee et al, 2003). Using random forest modeling, which is a decision tree based machine learning method (Breiman, 2001) established as an effective tool for addressing the complexity of modelling with genomic data (Sun, 2009; Yang et al, 2010b; Dietterich, 2000), we first tested the collective ability of a set of SNPs derived from previous GWAS on CAC (SNP Set-1) in predicting advanced CAC with data from the ClinSeq® study (Biesecker et al, 2009) previously published in (Sen et al, 2014b,a). Upon deriving an alternative SNP set (SNP Set-2) and comparing its predictive ability to SNP Set-1 within the ClinSeq® discovery cohort with and without clinical data, we used data from the Framingham Heart Study (FHS) to test whether we could replicate the observed predictive patterns.…”

Histology-directed and imaging mass spectrometry: An emerging technology in ectopic calcification

“…Nevertheless it is still unclear whether calcification affects particular matrix components in specific organs/tissues, whereas other areas remain unaffected and which molecules/pathways could be targeted for pharmacological approaches. To address these questions, investigations performed so far have looked at the specific expression/localization of already known proteins [2] or have used cell lines and tissue extracts to pick up unknown gene/proteins by means of “omic” techniques [3-5]. However, the major difficulty of these techniques is the ability to analyse a large number of proteins without losing the morphology and the tissue architecture and, even more importantly, to discriminate which proteins belong to normal or to pathologic areas.…”