Identification of candidate genes involved in coronary artery calcification by transcriptome sequencing of cell lines
- Shurjo K Sen1,
- Jennifer J Barb2,
- Praveen F Cherukuri1,
- David S Accame1,
- Abdel G Elkahloun1,
- Larry N Singh1,
- Shih-Queen Lee-Lin1,
- NISC Comparative Sequencing Program1,
- Frank D Kolodgie3,
- Qi Cheng3,
- XiaoQing Zhao3,
- Marcus Y Chen4,
- Andrew E Arai4,
- Eric D Green1,
- James C Mullikin1,
- Peter J Munson2 and
- Leslie G Biesecker1Email author
© Sen et al.; licensee BioMed Central Ltd. 2014
Received: 11 October 2012
Accepted: 4 March 2014
Published: 14 March 2014
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.
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.
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.
KeywordsCoronary artery calcification RNA-Seq Lymphoblastoid cell lines Transcriptome profiling
Coronary Artery Disease (CAD) is a leading cause of mortality worldwide. The etiology of CAD is influenced by lifestyle, genetics, and gut microbiome [1, 2] although the balance of these contributors remains unclear. While numerous GWAS projects have explored the genetics of CAD susceptibility , comparatively less is known about gene expression changes in this disease . This is partly because CAD is a complex phenotype involving multiple physiological systems. Consequently, no single tissue may fully reflect the network of gene expression changes underlying the disease .
In addition, current knowledge of the CAD transcriptome is based on gene expression microarrays, a technology that is useful but has several limitations. Recently, 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) . In addition, the high resolution of RNA-Seq can facilitate discoveries of transcript dysregulation that have been missed by previous technologies. Here, 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 . As a patient surrogate for gene expression, we used Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs), which have been shown in multiple studies to reliably reflect gene expression signatures , particularly those associated with nearby cis-acting genomic polymorphisms (expression quantitative trait loci or eQTLs) .
Here, we used RNA-Seq to compare LCLs from matched CAC cases and controls in the ClinSeq® project , and report a set of candidate genes for CAD that show differential expression and splicing variation between the groups, as well as novel transcripts that may have relevance for disease. RNA-Seq results were experimentally and technically replicated by repeating the cell culture part of our experiments and then measuring gene expression independently using expression microarrays. At the network level, we demonstrate that these differentially expressed genes are enriched for prior CAD association as well as for biological pathways with direct relevance to atherosclerosis.
An expanded Methods section is provided in Additional file 1.
Coronary calcification scoring
CAC scoring was performed using multi-slice computed tomography (Aquilion One, Toshiba Medical Systems, Japan or Lightspeed VCT, General Electric Healthcare, Waukesha, Wisconsin) with the use of prospective electrocardiographic gating. Calcification was quantified using the Agatston scoring method  on a dedicated workstation (VitreaFX or GE Advantage). The study was approved by the NHGRI Institutional Review Board and all subjects gave written informed consent.
Cell culture, RNA-Seq library preparation, and sequencing
LCLs were established by Epstein-Barr Virus (EBV) inoculation of B-lymphocytes using standard procedures, grown to a density of 2–3 × 105 cells/ml, harvested when the culture reached 107 cells in total (5–6 passages) and stored at -80°C. Total RNA from 4 × 106 cells was extracted using the RNeasy Mini kit (Qiagen). RNA integrity was assessed using an Agilent Bioanalyzer (Agilent). RNA-Seq libraries were constructed using a custom protocol (see Additional file 1). Sequencing was done on Illumina GAIIx sequencers (Illumina), collecting two lanes of 51 bp reads for each library.
RNA-Seq data processing
Reads were aligned to the human genome (hg18) using TopHat . The TopHat output SAM file was converted to BED format using the bamToBed script from the BEDTools package . The coverageBed script from BEDTools was used to count reads mapping to individual exons in the RefSeq database (see Additional file 1). A total count for each transcript in this database was obtained by adding the counts for its constituent exons. These counts, after filtering and normalization (see Additional file 1), were used for statistical analysis of differential expression. We confirmed the subject identity in all RNA-Seq libraries by genotyping a subset of expressed SNPs from the corresponding genomic DNA. This indicated some contamination in the data for subject 133871; hence, this subject was excluded from further RNA-Seq analyses. The transcript-level RNA-Seq counts were first analyzed using a two level, one-way ANOVA to compare the expression in the case and control groups. Concurrently, the count data were analyzed using the edgeR Bioconductor package with default settings using the moderated tagwise dispersion option and the prior. N parameter set to 4.
Affymetrix exon array experiment
For each sample, two micrograms of total RNA were used in conjunction with the Whole-Transcript Expression Analysis protocol for Affymetrix Human Exon 1.0 ST arrays. Arrays were scanned using an Affymetrix Gene Chip Scanner 3000 and intensities were calculated using Affymetrix AGCC software. RMA (Robust Multichip Average) signal intensities were calculated using Affymetrix Expression Console, converted to arithmetic scale, and transformed using an adaptive variance-stabilizing, quantile-normalizing transformation that was scaled to match the transformation used on the RNA-Seq data. A two level, one-way ANOVA was run on the microarray expression results.
Western blot analysis of IGLL5
A small piece of human carotid atherosclerotic plaque was frozen in liquid nitrogen and crushed to fine powder using an alloy tool steel mortar and pestle set that was pre-cooled in liquid nitrogen. The tissue powder was transferred to an ice-cold microcentrifuge tube and ice-cold cell lysis buffer (RayBiotech #0103004-L) containing protease inhibitor cocktail (Sigma P8430) was added; the tissue weight to buffer volume ratio was 0.1 g/0.3 ml. The samples were vortexed for 20 seconds at 10-minute intervals for 30 mins (and were kept on ice when not being vortexed). Samples were then centrifuged at 14000 g for 15 minutes at 4°C; the supernatant was collected in new microcentrifuge tubes. Protein concentrations in the supernatant were quantified by using Micro BCA Protein Assay Kit (Pierce #23225) according to manufacturer’s instructions. 50ug of protein from each sample was separated on a 4 – 20% polyacrylamide gel (Bio-Rad, #161-1159) with Tris-glycine – SDS buffer (Bio-Rad, #161-0732). The proteins were transferred onto Immuno-Blot PVDF membranes (Bio-Rad, #162-0174). The membrane was blocked with 5% non-fat dry milk in Tris-buffered saline Tween 20 (0.5%) for 1 hour at room temperature, then incubated with primary antibody (anti human IGLL5, Abgent #AP18459b) at a concentration of 1:200 overnight at 4°C. The membranes were washed with Tris-buffered saline Tween 20 (0.5%) three times, incubated with a secondary peroxidase – linked antibody (anti rabbit IgG HRP conjugate, Bio-Rad #170-6515) for two hours at room temperature and then washed three times with Tris-buffered saline Tween 20 (0.5%). The reactive bands were visualized by a chemiluminescence kit (Bio-Rad, #170-5040) on Kodak BioMax Light film. The intensity of each IGLL5 band was analyzed by using the Chemidoc XRS system with Western blot analysis software (Bio-Rad). The final intensity was adjusted by individual loading control intensity readings (beta-actin).
Detection of novel transcripts
The Ensembl, AceView, ccDs, knownGenes, refGene, tRNA and rnaGene annotation tables were downloaded in GTF format from the UCSC Genome Browser (http://www.genome.ucsc.edu) and combined into one master annotation file using the Cuffmerge tool in Cufflinks . Transcripts were assembled using RNA-Seq data from a separate LCL library with extremely high depth of sequencing (~200 million read pairs) using Cufflinks with the -M option to mask transcripts in the combined annotation file. These putative novel transcripts were then used as a reference annotation to run Cuffdiff, with the same BAM files from the case and control subjects that were used for quantifying gene expression. Loci detected as significant after Benjamini-Hochberg multiple testing correction were visually inspected by viewing the raw sequencing data (in BAM and BigWig format) on the UCSC Genome Browser with all gene and gene prediction tracks (and the ENCODE gene regulation track) turned on.
Detection of alternatively spliced transcripts
where y was the normalized read count for an exon, A i was the fixed treatment effect for 1 through i treatments (in this case, the case or control status), βj (i) was the random sample effect for sample j within treatment, C k was the fixed exon effect for 1 through k exons within a transcript, AC ik was the fixed interaction “treatment X exon” effect and ϵ was the error factor. The ANOVA p-value for p-AC (which indicates the strength of the exon-treatment interaction) was then used to select for exons showing significantly different usage between cases and controls. Independently, the cuffdiff algorithm  was also used to detect alternatively spliced transcripts.
Assessment of CAD burden and RNA-Seq experimental design
Clinical data for 32 subjects
Discovery cohort (RNA-Seq + Microarray)
Validation cohort (Microarray)
Analyses of gene expression with RNA-Seq data
Validation of RNA-Seq results using affymetrix arrays
To address the effects of variation introduced by experimental procedures, we repeated the cell culture and RNA extraction steps for all 16 cell lines used for the RNA-Seq, starting from previously frozen stocks. We then measured gene expression in these newer cultures using Affymetrix Human Exon 1.0 ST microarrays. Previous studies have demonstrated that these two methods of expression measurement (RNA-Seq and microarray) show strong agreement when applied to the same RNA sample . Here, despite the fact that two additional passages of the cell lines took place in between the separate experiments, comparisons of the sixteen pairs of RNA-Seq and microarray results from the same subjects still showed high correlation (average R = 0.73, p < 0.001) (Additional file 1: Figure S1). Hence, we conclude that in vitro procedures did not appear to cause significant variability in the gene expression profiles of the cell lines.
Analyses of transcription at unannotated genomic regions
Novel transcripts detected by Cufflinks/Cuffmerge/CuffDiff pipeline
Fold change (case/control)
Corrected p value
Detection of candidate genes for alternative splicing
Differentially expressed genes are enriched for CAD association and disease-relevant functions
We next determined if differentially expressed genes showed an overall relationship with cardiovascular disease. To investigate this, we first combined previously compiled lists of CAD-associated genes [30, 31]. From the combined list, we identified 3424 genes which showed expression in our RNA-Seq data. From this list, 43 genes are present in the list of 186 differentially expressed transcripts in our results. Statistically, this represented a highly significant enrichment of CAD-associated genes (one-tailed Fisher’s Exact Test, p = 0.0002; Additional file 1: Figure S4), suggesting that gene expression differences we detected by comparing cell lines from CAC cases and controls showed an overall relationship with CAD.
At the network level, differentially expressed genes may identify biological pathways that are perturbed in CAD. To investigate this hypothesis, we used Ingenuity Pathways Analysis™ (IPA) software, which performs systems biology analysis using a large repository of previously documented gene-gene interactions and functional annotations (Additional file 3). The highest-scoring network (IPA score = 48) in the list of 186 differentially expressed transcripts included the term “Cardiovascular System Development and Function”, further highlighting the relationship of differential expression in this study with CAD. Interestingly, the second highest-scoring network (IPA score = 36) contained the function “Lipid Metabolism”, suggesting another possible mechanism through which gene expression differences may be related to CAD etiology. Finally, Gene Ontology (GO) term analysis of the 186 transcripts (using only LCL-expressed genes as background to avoid bias), detected a number of networks related to cyclic AMP metabolism (Additional file 2: Table S9).
We present here results from a pilot project for the use of RNA-Seq and cell lines in studying CAC gene expression. We used gene expression microarrays and cell culture replication for technical and experimental validation, respectively, of our initial results from this new technology. This project should be viewed in context of its scope and limitations; analyses of much larger numbers of LCLs will be required before any definitive statements can be made about the functional role in CAC of genes that we detected. However, our primary objective in this study was not functional analysis of such genes, but rather to identify candidate dysregulated genes in CAC using two relatively unexplored tools that may be useful overall in CAD research. By choosing LCLs from subjects who represent extreme outliers for the calcification score distribution, we hypothesized that between-group differences that are relevant for CAD may be detected. Evidence for this is reflected in the significant enrichment of CAD-associated genes in our results and further underscored in our IPA results, which show that differentially expressed genes we detected are concentrated in functional networks directly connected to CAD. However, we do not suggest that LCLs mirror in any way the transcriptional landscape of the atherosclerotic lesion itself. Rather, we hypothesize that our results represent eQTL-associated gene expression variants, which are stably propagated in these cell lines, underlying the observed differences between the case and control groups. We show that the combination of RNA-Seq and microarrays can be used to obtain useful results with a comparatively small sample size. In the near future, we anticipate that rapidly falling sequencing costs and development of user-friendly data analysis tools will soon make RNA-Seq the preferred alternative for transcriptome studies.
As the scope of this pilot project did not include functional validation assays, we refrain from speculating on the specific mechanisms by which individual genes may be involved in atherosclerosis development. However, as to the best of our knowledge, this study represents the first whole-transcriptome analysis specifically focusing on coronary artery calcification, and differentially genes that we found include many previous CAD candidates (such as MMP7, EPAS1, ESAM, CASP1, GUCY1A3, CLCN4, LEF1, ENPP5, and ZHX2), some observations may be relevant to state here. Interestingly, we detected differential expression of ZHX2, a regulator of plasma lipid metabolism that is the closest gene to the top SNP locus from a recent atherosclerosis GWAS meta-analysis . At the gene family level, the GIMAP family is enriched, with four genes (GIMAP1, GIMAP4, GIMAP5, and GIMAP7). Perhaps not by coincidence, multiple GIMAP family members have also been reported in a set of 128 genes (the “A-module”) that is associated with atherosclerosis development. Overall, the GIMAP family has a crucial role in the development and function of T lymphocytes, which have an important role in the etiology of atherosclerosis . Finally, 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 .
In summary, in this project we piloted the study of coronary artery calcification using cell lines as a patient surrogate for gene expression. We demonstrate that with careful experimental design and secondary validation of results, statistically significant results can be obtained with RNA-Seq even from a small sample size. In addition to canonical gene expression, we focused on alternative splicing and novel transcript discovery, two areas of the CAD transcriptome that may benefit the most from further RNA-Seq analyses. Studies using larger numbers of LCLs along with follow-up experiments will be needed to validate differentially expressed genes from this pilot study as true CAC biomarkers; this will become possible in the near future as we deploy RNA-Seq to the entire ClinSeq® cohort.
Lymphoblastoid cell line
Coronary artery disease
Coronary artery calcification
Genome-wide association study
False discovery rate.
The authors thank Peter Chines, Nancy Hansen, Jamie Teer, and Aaron Quinlan for computational analyses, Robert Blakesley, Alice Young, Michael Erdos, Marjorie Lindhurst, Jacquelyn Idol, and Jennifer Johnston for help with experimental procedures, Julia Fekecs for help with preparing high-resolution graphics, Soma Chowdhury for proofreading, and Flavia Facio, David Ng, Clesson Turner, and the ClinSeq® clinical support and nursing staff for their help with the clinical aspects of this study. We thank Eric Olivares and the SEQanswers community (http://SEQanswers.com) for providing valuable advice. Above all, we are grateful to the many participants of the ClinSeq® study. This study is supported by funds from the Intramural Research Program of the National Institutes of Health.
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