Richter, O. | Search bioRxiv

bioRxiv2024

Mitochondrial reactive oxygen species cause arrhythmias in hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease and caused by genetic variants that often increase sarcomeric Ca2+ sensitivity. While Ca2+ sensitization explains diastolic dysfunction, the genesis of ventricular arrhythmias is unresolved. Here, we show that HCM mutations or pharmacological interventions that increase myofilament Ca2+ sensitivity generate bioenergetic mismatch and oxidative stress during {beta}-adrenergic stimulation which provide a trigger and a substrate for arrhythmias. For any given sarcomere shortening that produces work and consumes ATP, less Ca2+ stimulates the Krebs cycle to maintain mitochondrial NADH. This reverses the mitochondrial transhydrogenase to regenerate NADH from NADPH, supporting ATP production at the cost of NADPH-dependent antioxidative capacity. The ensuing overflow of reactive oxygen species (ROS) from mitochondria and glutathione oxidation induce spontaneous Ca2+ release from the sarcoplasmic reticulum and Ca2+ waves, well-defined triggers of arrhythmias. Furthermore, transhydrogenase-dependent ROS formation slows electrical conduction during {beta}-adrenergic stimulation in vivo, providing a substrate for arrhythmias. Chronic treatment with a mitochondrially-targeted ROS scavenger abolishes the arrhythmic burden during {beta}-adrenergic stimulation in HCM mice in vivo, while inducing mitochondrial ROS with a redox cycler is sufficient to induce arrhythmias in wild-type animals. These findings may lead to new strategies to prevent sudden cardiac death in patients with HCM.

physiology↗

bioRxiv2024

Building a small brain with a simple stochastic generative model

The architectures of biological neural networks result from developmental processes shaped by genetically encoded rules, biophysical constraints, stochasticity, and learning. Understanding these processes is crucial for comprehending neural circuits structure and function. The ability to reconstruct neural circuits, and even entire nervous systems, at the neuron and synapse level, facilitates the study of the design principles of neural systems and their developmental plan. Here, we investigate the developing connectome of C. elegans using statistical generative models based on simple biological features: neuronal cell type, neuron birth time, cell body distance, reciprocity, and synaptic pruning. Our models accurately predict synapse existence, degree profiles of individual neurons, and statistics of small network motifs. Importantly, these models require a surprisingly small number of neuronal cell types, which we infer and characterize. We further show that to replicate the experimentally-observed developmental path, multiple developmental epochs are necessary. Validation of our models predictions of the synaptic connections using multiple reconstructions of adult worms suggests that our model identified the fundamental "backbone" of the connectivity graph. The accuracy of the generative statistical models we use here offers a general framework for studying how connectomes develop and the underlying principles of their design.

neuroscience↗