Plakophilin-2 (PKP2) is a component of the desmosome and known for its role in cell–cell adhesion. Mutations in human PKP2 associate with a life-threatening arrhythmogenic cardiomyopathy, often of right ventricular predominance. Here, we use a range of state-of-the-art methods and a cardiomyocyte-specific, tamoxifen-activated, PKP2 knockout mouse to demonstrate that in addition to its role in cell adhesion, PKP2 is necessary to maintain transcription of genes that control intracellular calcium cycling. Lack of PKP2 reduces expression of Ryr2 (coding for Ryanodine Receptor 2), Ank2 (coding for Ankyrin-B), Cacna1c (coding for CaV1.2) and Trdn (coding for triadin), and protein levels of calsequestrin-2 (Casq2). These factors combined lead to disruption of intracellular calcium homeostasis and isoproterenol-induced arrhythmias that are prevented by flecainide treatment. We propose a previously unrecognized arrhythmogenic mechanism related to PKP2 expression and suggest that mutations in PKP2 in humans may cause life-threatening arrhythmias even in the absence of structural disease.
Neural coupled oscillators are a useful building block in numerous models and applications. They were analyzed extensively in theoretical studies and more recently in biologically realistic simulations of spiking neural networks. The advent of mixed-signal analog/digital neuromorphic electronic circuits provides new means for implementing neural coupled oscillators on compact, low-power, spiking neural network hardware platforms. However, their implementation on this noisy, low-precision and inhomogeneous computing substrate raises new challenges with regards to stability and controllability. In this work, we present a robust, spiking neural network model of neural coupled oscillators and validate it with an implementation on a mixed-signal neuromorphic processor. We demonstrate its robustness showing how to reliably control and modulate the oscillator’s frequency and phase shift, despite the variability of the silicon synapse and neuron properties. We show how this ultra-low power neural processing system can be used to build an adaptive cardiac pacemaker modulating the heart rate with respect to the respiration phases and compare it with surface ECG and respiratory signal recordings from dogs at rest. The implementation of our model in neuromorphic electronic hardware shows its robustness on a highly variable substrate and extends the toolbox for applications requiring rhythmic outputs such as pacemakers.
Degradation of cellular material by lysosomes is known as autophagy, and its main function is to maintain cellular homeostasis for growth, proliferation and survival of the cell. In recent years, research has focused on the characterization of autophagy pathways. Targeting of autophagy mediators has been described predominantly in cancer treatment, but also in neurological and cardiovascular diseases. Although the number of studies is still limited, there are indications that activity of autophagy pathways increases under arrhythmic conditions. Moreover, an increasing number of antiarrhythmic and non-cardiac drugs are found to affect autophagy pathways. We, therefore, suggest that future work should recognize the largely unaddressed effects of antiarrhythmic agents and other classes of drugs on autophagy pathway activation and inhibition.
Central pattern generators (CPGs) are neuronal networks that autonomously produce patterns of phase-locked activity. The need for bioelectronic implants that adapt to physiological feedback calls for novel methods for designing synthetic CPGs that respond identically to their biological counterparts. Here, we consider optimization-based parameter estimation for identifying network parameters that give rise to activity with specific temporal properties. We demonstrate that reducing a network to the phase resetting curves (PRCs) of its component neurons allows for the sequential parameter estimation of each single neuron separately. In this way, the challenges associated with estimating all network parameters simultaneously may be avoided. We highlight a possible application of our approach by estimating parameters of a CPG emulating the phase-locked activity associated with ECG data. This work paves the way for the design of synthetic networks which may be interfaced with nervous systems.
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