Background
Circulating cell-free DNA (cfDNA) is emerging as a powerful tool for diagnosing and monitoring diseases. It has been successfully used in clinical practice for noninvasive prenatal testing and liquid biopsy for cancer, and its utility in graft rejection is being investigated [
1‐
6]. However, these genetic-based approaches are not applicable for situations where cfDNA originates from tissues with a normal genome. DNA methylation, a stable tissue-specific epigenetic modification, has recently been investigated for assessing the tissue of origin of cfDNA. We and others have established deconvolution methods for tissue fractions using whole-genome and target-enrichment DNA methylation methods [
7‐
13]. In addition, single-marker assays for a variety of tissues, including the pancreas, brain, liver, colon, and heart, have been reported [
8,
13‐
15].
Cardiovascular diseases, including myocardial infarctions (MIs), are the leading causes of death worldwide. MIs are known to be associated with cell death. A previous study has shown that the concentration of cfDNA is elevated in MI patients, with series sampling showing that the cfDNA level usually peaks later than creatine kinase-MB (CK-MB), but the source of the increased cfDNA is not clearly understood [
16]. In a recent landmark study, Zemmour et al. [
14] has shown that DNA from dying cardiomyocytes can be released into the blood as cfDNA. The marker FAM101A has been reported to be a cardiomyocyte-specific unmethylated marker which increases in the plasma of MI patients. However, no heart-specific hypermethylated marker has been reported. Furthermore, as the diagnosis of cardiovascular diseases is time-sensitive, it is important to develop PCR-based assays for a heart-specific marker.
Here, we applied a genomic DNA methylation sequencing-based technique, methylated CpG tandem amplification and sequencing (MCTA-seq) [
5], to explore heart-specific hypermethylated markers and dynamic changes of heart-derived DNA in the blood of MI patients, and we also developed a droplet digital PCR (ddPCR) assay for detecting MI.
Methods
Sample collection
The study was approved by the Ethics Committee of Fuwai Hospital (Ethics No. 2018-1007). All subjects provided written informed consents for the collection of samples and subsequent analyses before inclusion in the study.
We collected tissue and plasma samples at Fuwai Hospital, Chinese Academy of Medical Science. Three pairs of left atrial and left ventricular heart tissue samples were obtained from donors who died for reasons other than cardiovascular diseases (3 males; mean age, 25.3 ± 2.1 years). Three sets of plasma samples were obtained from MI patients who were defined according to the fourth universal definition of myocardial infarction [
17], with the exclusion criteria as no troponin elevation throughout the disease course, complicated with other diseases which also present with chest pain and elevated troponin such as aortic dissection or pulmonary embolism. These sets included (i) cohort 1: plasma samples obtained from patients (n = 20) after percutaneous coronary intervention (PCI), (ii) cohort 2: three series time points of plasma samples (n = 60) obtained from patients (n = 20) upon hospital admission (D0), 1 day after PCI (D1), and 2 days after PCI (D2), and (iii) cohort 3: plasma samples obtained from MI patients within 24 h of symptom onset upon hospital admission (n = 116); we also collected plasma of control individuals (n = 25), who were recruited from physical examination center of Fuwai hospital and had no history or symptoms of myocardial infarction, pulmonary embolism, aortic dissection or other significant diseases. The sample size of cohort 3 was determined using the software MedCalc (version 16.8.4). We applied MCTA-seq for cohort 1 and 2, and the
CORO6 ddPCR assay for cohort 3. All MCTA-seq results passed the quality control criterion as total molecular counts of 10,000, and all samples for the ddPCR assay were experimentally successful; thus none samples were excluded. The clinical characteristics of the patients were shown in Additional file
2: Table S1.
MCTA-seq data of nine tissues, i.e., the liver (n = 3), muscle (n = 2), lung (n = 2), stomach (n = 2), colon (n = 2), kidney (n = 2), pancreas (n = 2), skin (n = 2), and WBCs (n = 81), as well as the plasma of normal individuals (n = 202) and cancer patients (n = 229 for CRC and n = 42 for HCC), were retrieved from our previous studies [
5‐
7].
Blood sample processing
To obtain plasma, 4 mL peripheral blood was collected using EDTA anticoagulant tubes and the plasma samples were prepared within 6 h. The blood tube was centrifuged at 1350×g for 12 min at room temperature, and then the plasma was transferred to a 15-mL tube and centrifuged at 1350×g for 12 min, before the supernatant was transferred to a 1.5- or 2-mL tube and centrifuged at 13,500×g for 5 min. Finally, the plasma supernatant (approximately 2 mL) was transferred to a 1.5- or 2-mL new tube and immediately stored at − 80 °C.
DNA extraction and library construction
Genomic DNA was extracted from WBCs and tissues using a DNeasy Blood & Tissue Kit (Qiagen, 69504) according to the manufacturer’s protocol. For MI patients and control subjects, cfDNA was extracted using a QIAamp Circulating Nucleic Acid Kit (Qiagen, 55114). For MCTA-Seq library construction, the procedures were described previously [
5‐
7]. In brief, after bisulfite conversion (Zymo Research, D5030), cfDNA was subjected to the MCTA-Seq three-steps amplification, including (i) 1 cycle of amplification using a random primer to obtain the semi-amplicon, (ii) 1 cycle of amplification using a targeting primer characterized as having CGCGCGG at its 3′ end to obtain the full-amplicon, and (iii) 14 cycles of exponential amplification using tail primers corresponding to Illumina TrueSeq adapters (see details in Additional file
1: Methods). The final library was sequenced on an Illumina HiSeq Xten platform to generate 150-bp paired-end reads.
Sequencing data processing
The R2 reads in FASTQ format procession were processed and filtered as previously described [
5‐
7]. We focused on the fully methylated molecules (FMM) amplified from a CGCGCGG as the unit for calculation. The methylation value is calculated as the number of FMMs normalized by the total number of reads uniquely mapped to the whole genome, and expressed as methylated alleles per million mapped reads (MePM) for tissue samples and unique molecular identifier-adjusted MePM (uMePM) for plasma samples [
5‐
7].
Identification of heart-specific methylation markers
Heart-specific markers were selected by considering the MCTA-Seq methylation sequencing data of all CCGCGCGG sites within CGIs. We aimed to identify markers that give the highest signal-to-noise ratio. For a heart cfDNA methylation marker, the signal is the methylation value in the heart tissue, and the noise is the methylation level in the cfDNA. Plasma cfDNA is mainly derived from blood cells, and as we and others have previously shown, the main non-hemopoietic origin of cfDNA is the liver [
7]. To this end, we focused on three parameters: the heart-to-white blood cell methylation ratio, the heart-to-plasma methylation ratio and the liver methylation value. In addition, we wanted to make sure that the signal can be released to the blood, and thus we examined whether the methylation value increase in plasma of MI patients after PCI, in which previously studies have shown that the signal from cardial cells prominently increase [
14]. We consider that this increase will also indicate that the signal is derived from cardial cells but not other cell types such as fibroblast and endothelial cells in the heart tissue. The MCTA-Seq data of WBCs, normal plasma and the liver tissue were retrieved from our previous studies [
5‐
7].
The criteria were as follows:
1.
The average methylation value (MePM) in the heart tissue being 100-fold higher than that in WBCs (heart/WBC > 100);
2.
The average methylation value in the heart tissue being 100 times higher than that in normal human plasma (heart/Pn > 100);
3.
The average methylation value from the liver tissues being below 5 (liver < 5), as the liver has been shown to be the main nonhematopoietic source of plasma cfDNA;
The plasma from patients after PCI were used for validating that the methylation value of the loci significantly increased in comparison with the normal plasma.
Deconvolution analysis for the heart-derived cfDNA fraction
The following equation was used to deconvolute the cfDNA tissue mapping:
$$\overline{{{\text{MP}}}} {\text{i = }}\sum\limits_{k} {\overline{MT}_{ik} *P_{k} .}$$
The deconvoluted MCTA-seq data were analyzed as previously described [
7]. In this study, heart-specific markers were added to the equation. A total of 9 simultaneous equations representing 9 nonhematopoietic tissue types were generated to be solved. To further eliminate any effect from nonspecific methylation in WBCs, the average tissue fraction values in fourteen paired WBC samples (0.022%, 0, 0.28%, 0.002%, 0.019%, 0.003%, 0.2%, 0.014%, and 0.016% for the liver, lung, stomach, colon, kidney, pancreas, muscle, skin and heart, respectively) were subtracted from the measured tissue fractions. In addition, the measured tissue fractions that were lower than the average values plus three standard deviations of WBC samples (0.11%, 0, 1.62%, 0.023%, 0.0209%, 0.035%, 1.2%, 0.17%, and 0.2% for the liver, lung, stomach, colon, kidney, pancreas, muscle, skin and heart, respectively) were set to zero.
The CORO6 ddPCR assay
The
CORO6 ddPCR assay covered a genome region (Chr17: 27,942,532–27,942,630) located within the intragenic CGI of
CORO6. We designed two sets of primers and probes targeting to the methylated and unmethylated alleles, respectively, which allowed simultaneously quantification the methylated and unmethylated alleles in a one tube reaction. The sequences of the two groups of primers and probes are as follows: 5′-GGGAGATTAGAATTTTTGGAGTTTAGG-3′ (forward primer), 5′-CGAAACTCGCAATCCAACCTC-3′ (reverse primer), and 5′-FAM-AGATTTACGTCGTTTTAGCG-MGB-3′ (probe), for the methylated allele; and 5′-GGGAGATTAGAATTTTTGGAGTTTAGG-3′ (forward primer), 5′-CAAATCCCAAACAAAACTCACAATCCA-3′ (reverse primer), and 5′-VIC-AGATTTATGTTGTTTTAGTGGAGGT-MGB-3′ (probe), for the unmethylated allele. For each case, cfDNA extracted from 1 to 2 mL plasma was subjected to bisulfite conversion (Zymo Research, D5030), and then the purified DNA was divided into two replicates and subjected to the ddPCR assay which were described in Additional file
1: Methods in detail.
Custom R scripts and R packages were used to construct heatmaps and to perform statistical analysis. GraphPad Prism (PRISM version 5) software was used to generate boxplots, bar plots, and AUC curves and to perform statistical analysis for the nonmultiplex tests. A P value of 0.01 or 0.05 was set as the cutoff for significance.
Discussion
In this study, we conducted MCTA-Seq to identify heart-specific methylated markers and investigated the origin and dynamics of the increased cfDNA in MI patients. Among the identified markers, CORO6 shows the top performance. We developed a CORO6 ddPCR assay for detecting heart damage in blood.
MCTA-seq is suitable for screening cfDNA methylation markers since it detects thousands of hypermethylated CGIs in cfDNA in a semi-targeted manner. Among the detected CGIs, the
CORO6 locus emerged as the best heart-specific hypermethylation marker. The
CORO6 ddPCR assay detected approximate 20% methylation level in the heart and 0.015% in WBCs. As the heart tissue is composed of approximately 30% cardiomyocytes [
22], the ratio is estimated to be approximate 60% in cardiomyocytes. Zemmour et al. [
14] have previously described unmethylated FAM101A as the first heart-specific marker. Methylated
CORO6 was detected in a similar percentage of control individuals compared with unmethylated FAM101A (29% for the FAM101A sequencing-based assay and 20% for the
CORO6 ddPCR assay), indicating that the two loci have similar background levels in the blood. The signal of FAM101A is higher than that of
CORO6 in the cardiomyocytes (89% for FAM101A and ~ 60% for
CORO6). However, the amplicon length of the
CORO6 ddPCR assay (71 bp) was shorter than that of the FAM101A sequencing-based assay (90 to 100 bp). Since cfDNA is highly fragmented and bisulfite treatment further reduces the length, a short amplicon should give a higher signal than a long amplicon, particularly for cfDNA detection. FAM101A sequencing-based assay has shown an AUC value of 0.76 for detecting plasma before PCI, and the
CORO6 dd
PCR assay showed an AUC value of 0.68 (95% CI 0.59–0.78). We consider that the performance of the
CORO6 assay is comparable to that of the FAM101A sequencing-based assay for detecting heart-derived DNA.
An advantage of the
CORO6 ddPCR assay is that it is more rapid and convenient than the FMA101A sequencing-based assay. Zemmour et al. also developed a ddPCR assay for FAM101A. However, since the marker requires the simultaneous interrogation of six CpG sites crossing a relatively long distance, it is not possible to perform a standard ddPCR assay. Though the authors cleverly used two fluorescent probes to cover five CpG sites, the technical specificity of the ddPCR assay is still approximately 50-fold worse to the sequencing-based assay; thus, the performance of the FAM101A ddPCR assay is not satisfactory. In contrast, the
CORO6 assay showed high specificity comparable to the FAM101A sequencing-based assay, with a typical ddPCR design that interrogates 3 CpG sites using one 20–25 bp TaqMan probe. In normal plasma, the FAM101A ddPCR assay has been reported to show a specificity of 53%, while the
CORO6 ddPCR assay showed a specificity of 80% [
23]. In addition, comparing with a hypomethylation marker, a hypermethylation marker provides a technical advantage as relatively resisting to contamination from the unmethylated amplified PCR products, which are converted into unamplifiable products by the bisulfite treatment. The performance of the
CORO6 ddPCR assay may be further increased by optimizing the primer and probe, and by improvement of DNA methylation detection technique.
The
CORO6 ddPCR assay provides a simple method for investigating clinical situation with heart injure. Our study provides the first independent experimental validation of Zemmour et al.’s study showing the release of cardiac-derived cfDNA during MI [
14]. A recent report has shown elevation of cardiomyocyte-specific cfDNA in heart failure patients using the FMA101A ddPCR assay. The elevation of the total cfDNA level has been shown in uncontrolled hypertension; yet the source remains to be determined [
23]. Future investigation is needed for the usage of the heart-specific methylation marker in MI, heart failure and hypertension. In addition, the heart-specific methylation marker including the
CORO6 ddPCR assay may complement the genetic and sequencing-based cfDNA method for monitoring heart transplantation, which is quicker and able to distinguish between cardiac and coronary released donor cfDNA [
4]. It is also possible to further increase the specificity and sensitivity of the
CORO6 assay by testing regions adjacent to the one covered by our present ddPCR assay, by adding the antisense information, or by combining FAM101A or other types of molecules such as miR-208 and miR-499 [
24‐
27].
We showed that only a small portion of the increased cfDNA was derived from the heart in MI patients who underwent PCI. We made a similar finding in acute pancreatitis patients in whom the increased cfDNA was also mainly not derived from the pancreas [
7]. Recently, Moss et al. more precisely showed that the increased cfDNA in sepsis patients is mainly derived from granulocytes [
13]. Thus, it appears that elevation of cfDNA from WBCs is common in acute clinical situation and reflects an immune response. It is interesting that the total WBC count has been associated with the risk of coronary heart disease [
28], and an increase in the WBC count after an MI episode has been shown to be a predictor of worse patient prognosis [
29]. Increased cfDNA levels have also been shown to be a prognostic marker in a small cohort of MI patients [
30]. Distinguishing tissue of origin of the elevated cfDNA may provide more information for prognosis prediction.
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