Title |
Non-invasive detection of human cardiomyocyte death using methylation patterns of circulating DNA
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Published in |
Nature Communications, April 2018
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DOI | 10.1038/s41467-018-03961-y |
Pubmed ID | |
Authors |
Hai Zemmour, David Planer, Judith Magenheim, Joshua Moss, Daniel Neiman, Dan Gilon, Amit Korach, Benjamin Glaser, Ruth Shemer, Giora Landesberg, Yuval Dor |
Abstract |
Detection of cardiomyocyte death is crucial for the diagnosis and treatment of heart disease. Here we use comparative methylome analysis to identify genomic loci that are unmethylated specifically in cardiomyocytes, and develop these as biomarkers to quantify cardiomyocyte DNA in circulating cell-free DNA (cfDNA) derived from dying cells. Plasma of healthy individuals contains essentially no cardiomyocyte cfDNA, consistent with minimal cardiac turnover. Patients with acute ST-elevation myocardial infarction show a robust cardiac cfDNA signal that correlates with levels of troponin and creatine phosphokinase (CPK), including the expected elevation-decay dynamics following coronary angioplasty. Patients with sepsis have high cardiac cfDNA concentrations that strongly predict mortality, suggesting a major role of cardiomyocyte death in mortality from sepsis. A cfDNA biomarker for cardiomyocyte death may find utility in diagnosis and monitoring of cardiac pathologies and in the study of normal human cardiac physiology and development. |
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Geographical breakdown
Country | Count | As % |
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United States | 23 | 40% |
Israel | 5 | 9% |
India | 1 | 2% |
Saudi Arabia | 1 | 2% |
Colombia | 1 | 2% |
Australia | 1 | 2% |
Canada | 1 | 2% |
Unknown | 24 | 42% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 36 | 63% |
Scientists | 17 | 30% |
Practitioners (doctors, other healthcare professionals) | 3 | 5% |
Science communicators (journalists, bloggers, editors) | 1 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 198 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 35 | 18% |
Researcher | 31 | 16% |
Student > Master | 20 | 10% |
Other | 16 | 8% |
Student > Bachelor | 13 | 7% |
Other | 20 | 10% |
Unknown | 63 | 32% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 52 | 26% |
Medicine and Dentistry | 22 | 11% |
Agricultural and Biological Sciences | 21 | 11% |
Engineering | 9 | 5% |
Immunology and Microbiology | 4 | 2% |
Other | 18 | 9% |
Unknown | 72 | 36% |