The distribution of circulating microRNA and their relation to coronary disease

miRNA Profile in Human Plasma, Serum, Platelets, Mononuclear Cells and Whole Blood

There is minimal information defining miRNA expression in human blood and a complete screen of all known miRNAs has never been reported due to the limitations of current arrays and the cost of extensive qRT-PCR screening. Using blood samples from normal subjects and high-throughput qRT-PCR, the miRNA expression profile was determined for whole blood, isolated platelets, mononuclear cells, plasma and serum from five healthy subjects. Of the 852 miRNAs measured (Supplemental Table 1), the distribution of circulating miRNA that were most abundantly expressed in plasma and many of the blood derived sources is shown in Table 1. Gene expression is listed as cycle threshold value (Ct) consistent with RT-PCR-based data. Because the Ct values are listed, higher gene expression is reflected by a lower number. A complete list of miRNA expression for all sources is shown in Supplemental Table 2 (mean Ct value) and Supplemental Table 3 (mean delta Ct, accounting for housekeeping gene hsa-RNU1A-1). Unlike mRNA expression, it is currently unclear if miRNA data is more reliable when normalized with a housekeeping gene. This is especially germane for the cell-free plasma samples where a fixed volume was used and gene expression does not need to be normalized for cell count.

One-hundred and ninety four miRNAs were not expressed in any of the blood-based samples. Peripheral blood mononuclear cells (PBMCs) contained the highest number of miRNAs (658) followed by whole blood (609), platelets (448), serum (178) and plasma (147). As the abbreviated list shown in Table 1 demonstrates, while there is consistency between the groups, some miRNAs are more highly expressed in select sources. Sixty miRNAs were expressed in all five components. As the precise source of miRNA in plasma is not yet known, there was particular interest in comparing the expression between plasma and cellular miRNA patterns. Based on the significant overlap between the groups, it is difficult to determine the source for many of the specific plasma-derived miRNAs. It is not clear from the current data which miRNAs have a non-blood-based cellular source. Interestingly, miR-1185 and miR-548a-5p were much more abundantly expressed in plasma as compared to PBMCs or platelets, or only expressed in plasma. Although an interesting observation, the source of these two miRNAs cannot be determined from this data. In addition, some genes were expressed in platelets or PBMCs and not whole blood. This is likely due to the greater dilution used with PAXgene tubes and the loss of measurable expression of less abundant miRNAs.

Plasma miRNA expression in patients with and without significant coronary disease

The initial hypothesis of this study was that patients with significant coronary disease would have altered expression of plasma miRNA. Patients were divided into two groups (Table 2); 1) ≥70% coronary stenosis of any coronary artery or 2) <70 coronary stenosis. There were notable differences in expression of some of the miRNAs between these two groups (Table 3). By direct comparison, several plasma miRNAs were found to have over 2-fold increased expression in patients with significant coronary disease (≥70%) as compared to those with minimal coronary disease or normal coronary arteries (Figure 1). Initial statistical analysis demonstrated that anti-hypertensive therapy, smoking, and lipid lowering therapy have a positive association with coronary artery status (Table 2). Increased expression of miR-494, miR-769-3p and miR-490-3p was associated with ≥70% coronary stenosis. Next, the six variables were fit into a logistic regression analysis. As seen in Table 4, anti-hypertensive therapy, smoking and miR-769-3p were significantly associated with the coronary status.

Figure 1. Fold change in plasma miRNA expression between in two groups.

miRNAs shown had expression increased >2-fold between the groups; 1) ≥70% coronary stenosis of any coronary artery or 2) < 70 coronary stenosis.

Plasma miRNA expression: significant coronary disease vs. disease-free

We initially determined whether presence of significant coronary disease, as defined by ≥70% stenosis is associated with specific miRNA expression. A secondary question is whether presence/absence of coronary disease is associated with miRNA expression. To study this, patients were placed into one of three groups; 1) patients with CAD; at least one of the coronary arteries have ≥70% occlusion; 2) patients with minimal CAD; coronary occlusion 1%–<70%; and 3) coronary atherosclerosis-free, patients with no angiographically documented coronary artery stenosis. Because the numbers are small, a simple (non-statistical) comparison was made to determine trends. As seen in Figure 2 and supplemental table 4, 18 miRNAs had a varied expression pattern between group 1 and group 3. Seventeen miRNAs were upregulated at least 2-fold with only miR-1914 downregulated 0.5-fold. Interestingly, most miRNAs demonstrated a dose response with the greatest expression in patients with the most coronary disease and the least expression in disease-free patients (Supplemental table 4).

Figure 2. Fold change in plasma miRNA expression between three groups.

miRNAs shown had expression increased >2-fold or decreased <0.5-fold for the groups; 1) patients with CAD; at least one of the coronary arteries have ≥70% occlusion; 2) patients with atherosclerosis but not clinically significant CAD; coronary occlusion 1%–<70%; and 3) coronary atherosclerosis-free, patients with no angiographically documented coronary artery stenosis.

miRNA target predictions

Currently, the capacity to measure miRNAs far outpaces our ability to understand their function in a given tissue. However, to better understand the potential significance of our findings, we conducted analyses that predict targets of the miRNAs that were up- or downregulated in coronary disease using two methods; current publications (www.ncbi.nlm.nih.gov/pubmed/) and TARGETSCAN 5.1 (http://www.targetscan.org). Using these methods, results for miRNA targets varied between 3 to 369 targets per miRNA when identified. With the TARGETSCAN search, target genes for individual miRNAs were identified using the context score for specific sites within genes. The context score is the sum of site-type contribution, 3’ pairing contribution, local AU contribution, and position contribution. The lower the context score indicates the most highly predicted targets for each miRNA. By TARGETSCAN search, miRNAs miR-1914 and miR-7-2, had no target gene identified.

Detailed predictions for the miRNAs found to be significant in CAD are shown in Table 5. In broad terms, some miRNAs appear to target transcription factors, growth factors, cytokine regulation, transmembrane proteins, signal transduction pathways, and epigenetic pathways such as histone acetylases. Prediction results for specific miRNAs in TARGETSCAN include the following: miR-129-3p and miR-494 target HMGCS1; miR-150 targets MMP14; miR-150, and 92b target MMP16. Additionally, miR-1207 appears to target energy metabolism (most likely involving glucose metabolism).

We also analyzed the miRNAs that were unique to plasma, miR-1185-1 and miR-548a-5p-1. While many potential targets were identified, these miRNAs were predicted to target genes involved in controlling transcription factors, as well as several growth and cell cycle components.

Sample procurement

This study was approved by Mersin University Ethical Committee (06/05/2009, #6/144) and written consent was obtained from the subjects to test the hypothesis of whether coronary occlusion of ≥70% is associated with increased plasma miRNA expression levels. Upon enrollment, a study coordinator identified the presence of the following risk factors: (1) age, (2) male sex, (3) clinical history of diabetes, (4) clinical history of hypertension, (5) cigarette smoking, (6) clinical history of hypercholesterolemias, and (7) family history of coronary disease. Coronary angiograms were analyzed off-line in a blinded fashion with the use of digital calipers to measure stenosis severity, and stenosis was defined as a dichotomous variable: if a stenotic lesion was ≥70%, that vessel was counted as stenosed. The presence or absence of stenotic disease was also noted. Patients were ranked as having 0- to 3-vessel disease (number of coronary arteries with detectable atherosclerotic disease). For each patient, K₃EDTA arterial blood (5 mL) was collected just prior to coronary angiography. Blood samples were centrifuged (3,000 g) and 400 µl plasma samples were stored at -80°C until RNA isolation.

In a separate smaller study, blood was obtained from healthy consented volunteers (n=5; 3 female, 2 male, average age=45) at Boston University School of Medicine as previously described³³. The study was approved by Boston University IRB and written consent obtained from the subjects. All subjects were free of medications or supplements, and had no history of hypertension, diabetes, smoking, or hyperlipidemia. Blood was collected into PAXgene RNA tubes (Becton Dickinson, Franklin Lakes, NJ) for whole blood, into CPT tubes (Becton Dickinson) for peripheral blood mononuclear cells (PBMCs), into citrate tubes for platelets and plasma, and empty tubes for serum. Isolated platelets were prepared as previously described.

RNA isolation: Total RNA including miRNAs was isolated from 200 µl plasma samples using miRVana Paris Kit (Ambion, Austin, TX). The RNA samples were stored at -80°C until cDNA conversion.

cDNA conversion: Isolated RNA samples were converted to cDNA using miScript Reverse Transcription Kit (Qiagen, Germantown, MD). The RNA was converted to cDNA using the following conditions: 37°C for 60 min, 95°C for 5 min, and 4°C hold until further processing or storage. cDNA samples were kept at -80°C until PCR analysis.

Pre-amplification: Prior to PCR, cDNA samples were pre-amplified using Taqman PreAmp Master Mix (Applied Biosystems, Foster City, CA). PreAmp Master Mix and 0.2x Primers were added to the cDNA samples and pre-amplified as follows: 95°C for 10 min once, 95°C for 15 sec, 55°C for 30 sec, 70°C for 4 min (final three steps repeated for 14 cycles).

qRT-PCR: Quantitative Real-Time PCR reactions (qRT-PCR) were performed using the high-throughput BioMark Real-Time PCR system (Fluidigm, South San Francisco, CA). Pre-amplified cDNA samples were diluted with 0.1mM EDTA in TE Buffer (1:5) and mixed with Power Sybr Green PCR Master Mix (Applied Biosystems), AmpliTaq Gold DNA Polymerase (Applied Biosystems) and Sample Loading Reagent (Fluidigm), then pipetted into sample inlets of Dynamic Array 96.96 chips (Fluidigm). Assay Loading Reagent (Fluidigm) and primers (Qiagen) were mixed and pipetted into assay inlets of Dynamic Array 96.96 chips. The IFC Controller HX (Fluidigm) was used to distribute primers and samples into chip reaction wells for qRT-PCR by microfluidic delivery. Gene expression experiments performed at Mersin and Gaziantep Universities in Turkey. The data were normalized using RNU1A1. Coefficient variations were less than 10% for almost all of the assays. Plasma volumes for all samples were constant (200 µl) and all following steps such as cDNA, PreAmplification and qRT-PCR had the same volumes always for all samples.

Statistics

When examining miRNAs that had larger fold changes between the CAD groups (Figure 1 and Figure 2) and those more highly expressed in the circulation using similar bioinformatics methods (such as targetscan), there were many miRNA targets that are known to control the processes important in the development of atherosclerosis (list not shown).

Univariate and bivariate analysis

We initially summarized our data in different stratifications based on our outcome variables (coronary disease status). Next, we examined the bivariate relationship between the response variable and quantitative covariates using either two-sample t-test or Kruskal-Wallis test, where appropriate. Specifically, the t-test was conducted to test for a mean difference in quantitative demographic variables and miRNA expression level between two categories of coronary disease status. The pooled variance or the Satterthwaite’s method was used to estimate variance based on the equality of variance test. We employed Kruskal-Wallis test to determine whether there was any mean difference among groups in the scenario with three-category outcome variable.

Multivariate analysis

Tree-based methods have been increasingly applied to biological research such as microarray data analysis and genome-wide association studies^34,35. RandomForest is a flexible nonparametric approach, which consists of many decision trees from bootstrap samples. In this study, we constructed randomForest²⁵ in identifying relevant variables to our outcome variable using the randomForest package in R (2.10.1)³⁶. Furthermore, we also conducted logistic regression and multinomial logistic regression where appropriate³⁷.