Background: The benefits of exercise training in the cardiovascular system have been well accepted; however, the underlying mechanism remains to be explored. Here, we report the initial functional characterization of an exercise-induced cardiac physiological hypertrophy-associated novel long noncoding RNA (lncRNA).
Methods: Using lncRNA microarray profiling, we identified lncRNAs in contributing the modulation of exercise-induced cardiac growth that we termed cardiac physiological hypertrophy-associated regulator (CPhar). Mice with adeno-associated virus serotype 9 driving CPhar overexpression and knockdown were used in in vivo experiments. Swim training was used to induce physiological cardiac hypertrophy in mice, and ischemia reperfusion injury surgery was conducted to investigate the protective effects of CPhar in mice. To investigate the mechanisms of CPhar's function, we performed various analyses including quantitative reverse transcription polymerase chain reaction, Western blot, histology, cardiac function (by echocardiography), functional rescue experiments, mass spectrometry, in vitro RNA transcription, RNA pulldown, RNA immunoprecipitation, chromatin immunoprecipitation assay, luciferase reporter assay, and coimmunoprecipitation assays.
Results: We screened the lncRNAs in contributing the modulation of exercise-induced cardiac growth through lncRNA microarray profiling and found that CPhar was increased with exercise and was necessary for exercise-induced physiological cardiac growth. The gain and loss of function of CPhar regulated the expression of proliferation markers, hypertrophy, and apoptosis in cultured neonatal mouse cardiomyocytes. Overexpression of CPhar prevented myocardial ischemia reperfusion injury and cardiac dysfunction in vivo. We identified DDX17 (DEAD-Box Helicase 17) as a binding partner of CPhar in regulating CPhar downstream factor ATF7 (activating transcription factor 7) by sequestering C/EBPβ (CCAAT/enhancer binding protein beta).
Conclusions: Our study of this lncRNA CPhar provides new insights into the regulation of exercise-induced cardiac physiological growth, demonstrating the cardioprotective role of CPhar in the heart, and expanding our mechanistic understanding of lncRNA function, as well.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.120.050446 | DOI Listing |
Turk Kardiyol Dern Ars
January 2025
Department of Cardiology, University of Health Sciences, Derince Training and Research Hospital, Kocaeli, Türkiye.
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January 2025
Department of Human Science and Promotion of Quality of Life, San Raffaele Open University of Rome, 00166 Rome, Italy.
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December 2024
Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20122 Milan, Italy.
Even if rarely detected, right atrial (RA) masses represent a diagnostic challenge due to their heterogeneous presentation. Para-physiological RA structures, such as a prominent Eustachian valve, Chiari's network, and lipomatous atrial hypertrophy, may easily be misinterpreted as pathological RA masses, including thrombi, myxomas, and vegetations. Each pathological mass should always be correlated with adequate clinical, anamnestic, and laboratory data.
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December 2024
National Laboratory Astana, Nazarbayev University, Astana 010000, Kazakhstan.
Despite the high progress that has been made in the field of cardiology, the left ventricular assist device (LVAD) can still cause complications (thrombosis/bleeding) in heart failure (HF) patients after implantation. Complications develop due to the incorrect dose of antithrombotic therapy, due to the influence of the non-physiological shear stress of the device, and also due to inherited genetic polymorphisms. Therefore, the aim of our study is to identify the influence of the genetic polymorphisms on complication development in HF patients with implanted LVADs with prescribed antiplatelet therapy.
View Article and Find Full Text PDFSensors (Basel)
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Biofluids Laboratory, Perm National Research Polytechnic University, 614990 Perm, Russia.
Simulating the cardiac valves is one of the most complex tasks in cardiovascular modeling. As fluid-structure interaction simulations are highly computationally demanding, machine-learning techniques can be considered a good alternative. Nevertheless, it is necessary to design many aortic valve geometries to generate a training set.
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