Emerging Gene Correction Strategies for Muscular Dystrophies: Scientific Progress and Regulatory Impact

Muscular dystrophies comprise a heterogeneous cluster of inherited muscle degenerative disorders with the common feature of progressive muscle weakness. These represent good candidates for treatment with gene-based therapies. Progress in gene transfer technologies has raised hopes for successful therapeutic restoration of mutated genes such as dystrophin in Duchenne muscular dystrophy. Delivery to enough muscle cells, however, remains a challenge for a successful gene replacement therapy. Other approaches based on exon skipping to correct mutant dystrophin’s pre-mRNA splicing patterns have been tried, and partial restoration of dystrophin expression was reported in late-stage clinical trials, but full therapeutic efficacy is yet to be confirmed. The emergence of gene editing and its recent success in AIDS have opened a new therapeutic era for muscular dystrophies. This chapter will cover new gene correction strategies for muscular dystrophies and their regulatory challenges before they can become routine treatment modalities in the clinic

An Imperfect Dopaminergic Error Signal Can Drive Temporal-Difference Learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards