In silico study of the mechanisms of hypoxia and contractile dysfunction during ischemia and reperfusion of hiPSC cardiomyocytes.
Mohamadamin ForouzandehmehrMichelangelo PaciJari A K HyttinenJussi T KoivumäkiPublished in: Disease models & mechanisms (2024)
Interconnected mechanisms of ischemia and reperfusion (IR) has increased the interest in IR in vitro experiments using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). We developed a whole-cell computational model of hiPSC-CMs including the electromechanics, a metabolite-sensitive sarcoplasmic reticulum Ca2+-ATPase (SERCA) and an oxygen dynamics formulation to investigate IR mechanisms. Moreover, we simulated the effect and action mechanism of levosimendan, which recently showed promising anti-arrhythmic effects in hiPSC-CMs in hypoxia. The model was validated using hiPSC-CM and in vitro animal data. The role of SERCA in causing relaxation dysfunction in IR was anticipated to be comparable to its function in sepsis-induced heart failure. Drug simulations showed that levosimendan counteracts the relaxation dysfunction by utilizing a particular Ca2+-sensitizing mechanism involving Ca2+-bound troponin C and Ca2+ flux to the myofilament, rather than inhibiting SERCA phosphorylation. The model demonstrates extensive characterization and promise for drug development, making it suitable for evaluating IR therapy strategies based on the changing levels of cardiac metabolites, oxygen and molecular pathways.
Keyphrases
- high glucose
- endothelial cells
- heart failure
- protein kinase
- oxidative stress
- acute myocardial infarction
- diabetic rats
- cardiac surgery
- drug induced
- intensive care unit
- single molecule
- left ventricular
- big data
- acute kidney injury
- cerebral ischemia
- emergency department
- drug delivery
- molecular docking
- cell therapy
- ms ms
- acute ischemic stroke
- machine learning
- mesenchymal stem cells
- smooth muscle
- atrial fibrillation
- bone marrow
- cardiac resynchronization therapy
- septic shock
- subarachnoid hemorrhage
- deep learning