Cx45 is markedly enhanced in the failing heart. Up-regulation of Cx45 in conjunction with down-regulation of Cx43 could result in abnormal impulse propagation and generation of ventricular arrhythmias, thereby predisposing patients in heart failure to sudden cardiac death.
Aim: Gap junction intercellular communication (GJIC) and hemichannel permeability may have important roles during an ischemic insult. Our aim was to evaluate the effect of ischemia on gap junction channels and hemichannels. Methods: We used neonatal rat heart myofibroblasts and simulated ischemia with a HEPES buffer with high potassium, low pH, absence of glucose, and oxygen tension was reduced by dithionite. Microinjection, western blot, immunofluorescence, cell viability and dye uptake were used to evaluate the effects induced by dithionite. Isolated perfused rat hearts were used to analyse infarct size. Results: Short period with simulated ischemia reduced the ability to transfer a dye between neighbouring cells, which indicated reduced GJIC. Prolonged exposure to simulated ischemia caused opening of hemichannels, and cell death was apparent while gap junction channels remained closed. Connexin 43 became partially dephosphorylated and the total amount decreased during simulated ischemia. We were not able to detect the alternative hemichannel-forming protein, Pannexin 1, in these cells. The potential importance of Connexin 43 or Pannexin 1 hemichannels in ischemia-induced infarct in the intact heart was studied by perfusion of the heart in the presence of peptides that block one or the other type of hemichannels. The connexin-derived peptide, Gap26, significantly reduced the infract/risk zone ratio (control 48.7±4.2% and Gap26 19.4±4.1%, p<0.001), while the pannexin-derived peptide, 10Panx1, did not change infarct/risk ratio. Conclusion: Connexin 43 is most likely responsible for both closure of gap junction channels and opening of hemichannels during simulated ischemia in neonatal rat heart myofibroblasts. Opening of connexin 43 hemichannels during ischemia-reperfusion seems to be an important mechanism for ischemia-reperfusion injury in the heart. By preventing the opening of these channels during early ischemia-reperfusion the infarct size becomes significantly reduced.
Electrophysiological remodeling involving gap junctions has been demonstrated in failing hearts and may contribute to intercellular uncoupling, delayed conduction, enhanced arrhythmias, and vulnerability to sudden death in patients with heart failure. Recently, we showed that failing human hearts exhibit marked increases in connexin45 (Cx45) expression in addition to previously documented decreases in connexin43 (Cx43) expression. Each of these changes results in reduced gap junction coupling. The objective of the present study was to examine functional consequences of increased Cx45 in cardiac gap junctions. Transgenic mice with cardiac-selective overexpression of the developmentally downregulated cardiac connexin, connexin45 (Cx45OE mice) were subjected to in vivo electrophysiology studies in which an intracardiac catheter was used to induce ventricular arrhythmias in anesthetized mice, and in which ambulatory ECG monitoring was used to detect spontaneous arrhythmias in unanesthetized mice. Hearts were analyzed by TaqMan RT-PCR, immunostaining, immunoblotting, and echocardiography. Lucifer yellow and neurobiotin dye transfer was used to assess coupling in transgenic and control myocyte cultures. Cx45 mRNA was two orders of magnitude greater in Cx45OE mice. Cx45-immunoreactive signal at gap junctions increased twofold and total Cx45 protein by immunoblotting increased 25% in Cx45OE mice compared with nontransgenic littermate controls. Functionally, Cx45OE mice exhibited more inducible ventricular tachycardia than controls but did not exhibit any other functional or structural derangements as assessed by echocardiography. Ventricular myocytes isolated from Cx45OE mice exhibited diminished intercellular transfer of Lucifer yellow dye and increased transfer of neurobiotin, consistent with altered cell-to-cell communication. Thus increased myocardial expression of Cx45 results in remodeling of intercellular coupling and greater susceptibility to ventricular arrhythmias in vivo.
Tracer kinetic modelling, based on dynamic 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in humans and animals. Knowledge of the arterial input-function (AIF) is required for such measurements. Our aim was to explore two noninvasive machine learning-based models, for AIF prediction in a small-animal dynamic FDG PET study.7 tissue regions were delineated in images from 68 FDG PET/computed tomography mouse scans. Two machine learning-based models were trained for AIF prediction, based on Gaussian processes (GP) and a long short-term memory (LSTM) recurrent neural network, respectively. Because blood data were unavailable, a reference AIF was formed by fitting an established AIF model to vena cava and left ventricle image data. The predicted and reference AIFs were compared by the area under curve (AUC) and root mean square error (RMSE). Net-influx rate constants, K i , were calculated with a two-tissue compartment model, using both predicted and reference AIFs for three tissue regions in each mouse scan, and compared by means of error, ratio, correlation coe cient, P value and Bland-Altman analysis. The impact of di↵erent tissue regions on AIF prediction was evaluated by training a GP and an LSTM model on subsets of tissue regions, and calculating the RMSE between the reference and the predicted AIF curve.Both models generated AIFs with AUCs similar to reference. The LSTM models resulted in lower AIF RMSE, compared to GP. K i from both models agreed well with reference values, with no significant di↵erences. Myocardium was highlighted as important for AIF prediction, but AIFs with similar RMSE were obtained also without myocardium in the input data.Machine learning can be used for accurate and non-invasive prediction of an image-derived reference AIF in FDG studies of mice. We recommend the LSTM approach, as this model predicts AIFs with lower errors, compared to GP.
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