Endogenous but not sensory-driven activity controls migration, morphogenesis and survival of adult-born juxtaglomerular neurons in the mouse olfactory bulb.

The development and survival of adult-born neurons are believed to be driven by sensory signaling. Here, in vivo analyses of motility, morphology and Ca2+ signaling, as well as transcriptome analyses of adult-born juxtaglomerular cells with reduced endogenous excitability (via cell-specific overexpression of either Kv1.2 or Kir2.1 K+ channels), revealed a pronounced impairment of migration, morphogenesis, survival, and functional integration of these cells into the mouse olfactory bulb, accompanied by a reduction in cytosolic Ca2+ fluctuations, phosphorylation of CREB and pCREB-mediated gene expression. Moreover, K+ channel overexpression strongly downregulated genes involved in neuronal migration, differentiation, and morphogenesis and upregulated apoptosis-related genes, thus locking adult-born cells in an immature and vulnerable state. Surprisingly, cells deprived of sensory-driven activity developed normally. Together, the data reveal signaling pathways connecting the endogenous intermittent neuronal activity/Ca2+ fluctuations as well as enhanced Kv1.2/Kir2.1 K+ channel function to migration, maturation, and survival of adult-born neurons

Loss or gain of function? Effects of ion channel mutations on neuronal firing depend on the neuron type

IntroductionClinically relevant mutations to voltage-gated ion channels, called channelopathies, alter ion channel function, properties of ionic currents, and neuronal firing. The effects of ion channel mutations are routinely assessed and characterized as loss of function (LOF) or gain of function (GOF) at the level of ionic currents. However, emerging personalized medicine approaches based on LOF/GOF characterization have limited therapeutic success. Potential reasons are among others that the translation from this binary characterization to neuronal firing is currently not well-understood—especially when considering different neuronal cell types. In this study, we investigate the impact of neuronal cell type on the firing outcome of ion channel mutations.MethodsTo this end, we simulated a diverse collection of single-compartment, conductance-based neuron models that differed in their composition of ionic currents. We systematically analyzed the effects of changes in ion current properties on firing in different neuronal types. Additionally, we simulated the effects of known mutations in KCNA1 gene encoding the KV1.1 potassium channel subtype associated with episodic ataxia type 1 (EA1).ResultsThese simulations revealed that the outcome of a given change in ion channel properties on neuronal excitability depends on neuron type, i.e., the properties and expression levels of the unaffected ionic currents.DiscussionConsequently, neuron-type specific effects are vital to a full understanding of the effects of channelopathies on neuronal excitability and are an important step toward improving the efficacy and precision of personalized medicine approaches

A Recurrent Mutation in KCNA2 as a Novel Cause of Hereditary Spastic Paraplegia and Ataxia

The hereditary spastic paraplegias (HSPs) are heterogeneous neurodegenerative disorders with over 50 known causative genes. We identified a recurrent mutation in KCNA2 (c.881G>A, p.R294H), encoding the voltage-gated K+-channel, K(V)1.2, in two unrelated families with HSP, intellectual disability (ID), and ataxia. Follow-up analysis of >2,000 patients with various neurological phenotypes identified a de novo p.R294H mutation in a proband with ataxia and ID. Two-electrode voltage-clamp recordings of Xenopus laevis oocytes expressing mutant KV1.2 channels showed loss of function with a dominant-negative effect. Our findings highlight the phenotypic spectrum of a recurrent KCNA2 mutation, implicating ion channel dysfunction as a novel HSP disease mechanism.Peer reviewe

Predicting functional effects of ion channel variants using new phenotypic machine learning methods.

Missense variants in genes encoding ion channels are associated with a spectrum of severe diseases. Variant effects on biophysical function correlate with clinical features and can be categorized as gain- or loss-of-function. This information enables a timely diagnosis, facilitates precision therapy, and guides prognosis. Functional characterization presents a bottleneck in translational medicine. Machine learning models may be able to rapidly generate supporting evidence by predicting variant functional effects. Here, we describe a multi-task multi-kernel learning framework capable of harmonizing functional results and structural information with clinical phenotypes. This novel approach extends the human phenotype ontology towards kernel-based supervised machine learning. Our gain- or loss-of-function classifier achieves high performance (mean accuracy 0.853 SD 0.016, mean AU-ROC 0.912 SD 0.025), outperforming both conventional baseline and state-of-the-art methods. Performance is robust across different phenotypic similarity measures and largely insensitive to phenotypic noise or sparsity. Localized multi-kernel learning offered biological insight and interpretability by highlighting channels with implicit genotype-phenotype correlations or latent task similarity for downstream analysis