Integrative DNA, RNA, and Protein Evidence Connects TREML4 to Coronary Artery Calcification

Abstract

Coronary artery calcification (CAC) is a heritable and definitive morphologic marker of atherosclerosis that strongly predicts risk for future cardiovascular events. To search for genes involved in CAC, we used an integrative transcriptomic, genomic, and protein expression strategy by using next-generation DNA sequencing in the discovery phase with follow-up studies using traditional molecular biology and histopathology techniques. RNA sequencing of peripheral blood from a discovery set of CAC cases and controls was used to identify dysregulated genes, which were validated by ClinSeq and Framingham Heart Study data. Only a single gene, TREML4, was upregulated in CAC cases in both studies. Further examination showed that rs2803496 was a TREML4 cis-eQTL and that the minor allele at this locus conferred up to a 6.5-fold increased relative risk of CAC. We characterized human TREML4 and demonstrated by immunohistochemical techniques that it is localized in macrophages surrounding the necrotic core of coronary plaques complicated by calcification (but not in arteries with less advanced disease). Finally, we determined by von Kossa staining that TREML4 colocalizes with areas of microcalcification within coronary plaques. Overall, we present integrative RNA, DNA, and protein evidence implicating TREML4 in coronary artery calcification. Our findings connect multimodal genomics data with a commonly used clinical marker of cardiovascular disease.

Calcification around the Struts of a Sirolimus-eluting Stent Approximately 16 Months after Implantation in an Autopsy Case

“…It has been reported that CD68-positive cells (macrophages or monocytes) predominantly exist at sites of intimal calcification in the coronary artery in patients with chronic kidney disease (13). Moreover, a recent report demonstrated that TREML4, a gene found to be positively associated with coronary artery calcification in cohort studies, is localized in macrophages surrounding the coronary plaques complicated by calcification (14). These observations suggest that macrophages may have played an important role in the onset of peri-strut calcification after SES implantation in the present case.…”

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.…”

Genetics and Genomics of Coronary Artery Disease

“…For example, in vivo studies of specific cardiac and vascular cells/ tissues will benefit from improvements in RNA-Seq methodology that will enable extraction and sequencing of RNA from limited numbers of cells/tissues that are difficult to process [26]. RNA-Seq in left ventricular myocardial tissue has been used to systematically identify the disease status of heart failure patients using a limited training dataset of six individuals, including three controls, one ischemic heart disease (ISCH), and two dilated cardiomyopathy (DCM) patients and has been successfully applied to a much larger set of 313 individuals [27].…”