Biallelic pathogenic variants in PLPBP (formerly called PROSC) have recently been shown to cause a novel form of vitamin B6-dependent epilepsy, the pathophysiological basis of which is poorly understood. When left untreated, the disease can progress to status epilepticus and death in infancy. Here we present 12 previously undescribed patients and six novel pathogenic variants in PLPBP. Suspected clinical diagnoses prior to identification of PLPBP variants included mitochondrial encephalopathy (two patients), folinic acid-responsive epilepsy (one patient) and a movement disorder compatible with AADC deficiency (one patient). The encoded protein, PLPHP is believed to be crucial for B6 homeostasis. We modelled the pathogenicity of the variants and developed a clinical severity scoring system. The most severe phenotypes were associated with variants leading to loss of function of PLPBP or significantly affecting protein stability/PLP-binding. To explore the pathophysiology of this disease further, we developed the first zebrafish model of PLPHP deficiency using CRISPR/Cas9. Our model recapitulates the disease, with plpbp À/À larvae showing behavioural, biochemical, and electrophysiological signs of seizure activity by 10 days post-fertilization and early death by 16 days post-fertilization. Treatment with pyridoxine significantly improved the epileptic phenotype and extended lifespan in plpbp À/À animals. Larvae had disruptions in amino acid metabolism as well as GABA and catecholamine biosynthesis, indicating impairment of PLP-dependent enzymatic activities. Using mass spectrometry, we observed significant B6 vitamer level changes in plpbp À/À zebrafish, patient fibroblasts and PLPHP-deficient HEK293 cells. Additional studies in human cells and yeast provide the first empirical evidence that PLPHP is localized in mitochondria and may play a role in mitochondrial metabolism. These models provide new insights into disease mechanisms and can serve as a platform for drug discovery.

Pyridoxine-dependent epilepsy (PDE) is a severe neonatal seizure disorder and is here modeled in aldh7a1 -/- zebrafish. Mutant larvae display spontaneous..

Significant maturation of swimming in zebrafish (Danio rerio) occurs within the first few days of life when fish transition from coiling movements to burst swimming and then to beat-and-glide swimming. This maturation occurs against a backdrop of numerous developmental changes-neurogenesis, a transition from predominantly electrical to chemical-based neurotransmission, and refinement of intrinsic properties. There is evidence that spinal locomotor circuits undergo fundamental changes as the zebrafish transitions from burst to beat-and-glide swimming. Our electrophysiological recordings confirm that the operation of spinal locomotor circuits becomes increasingly reliant on glycinergic neurotransmission for rhythmogenesis governing the rhythm of tail beats. This

Many spinal circuits dedicated to locomotor control have been identified in the developing zebrafish. How these circuits operate together to generate the various swimming movements during development remains to be clarified. In this study, we iteratively built models of developing zebrafish spinal circuits coupled to simplified musculoskeletal models that reproduce coiling and swimming movements. The neurons of the models were based upon morphologically or genetically identified populations in the developing zebrafish spinal cord. We simulated intact spinal circuits as well as circuits with silenced neurons or altered synaptic transmission to better understand the role of specific spinal neurons. Analysis of firing patterns and phase relationships helped identify possible mechanisms underlying the locomotor movements of developing zebrafish. Notably, our simulations demonstrated how the site and the operation of rhythm generation could transition between coiling and swimming. The simulations also underlined the importance of contralateral excitation to multiple tail beats. They allowed us to estimate the sensitivity of spinal locomotor networks to motor command amplitude, synaptic weights, length of ascending and descending axons, and firing behaviour. These models will serve as valuable tools to test and further understand the operation of spinal circuits for locomotion.

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Rostro-caudal coordination of spinal motor output is essential for locomotion. Most spinal interneurons project axons longitudinally to govern locomotor output, yet their connectivity along this axis remains unclear. In this study, we use larval zebrafish to map synaptic outputs of a major inhibitory population, V1 (Eng1+) neurons, which are implicated in dual sensory and motor functions. We find that V1 neurons exhibit long axons extending rostrally and exclusively ipsilaterally for an average of 6 spinal segments; however, they do not connect uniformly with their post-synaptic targets along the entire length of their axon. Locally, V1 neurons inhibit motor neurons (both fast and slow) and other premotor targets including V2a, V2b and commissural pre-motor neurons. In contrast, V1 neurons make robust inhibitory contacts throughout the rostral extent of their axonal projections onto a dorsal horn sensory population, the Commissural Primary Ascending neurons (CoPAs). In a computational model of the ipsilateral spinal network, we show that this pattern of short range V1 inhibition to motor and premotor neurons is crucial for coordinated rostro-caudal propagation of the locomotor wave. We conclude that spinal network architecture in the longitudinal axis can vary dramatically, with differentially targeted local and distal connections, yielding important consequences for function.

Knowledge of the cell-type-specific composition of the brain is useful in order to understand the role of each cell type as part of the network. Here, we estimated the composition of the whole cortex in terms of well characterised morphological and electrophysiological inhibitory neuron types (me-types). We derived probabilistic me-type densities from an existing atlas of molecularly defined cell-type densities in the mouse cortex. We used a well-established me-type classification from rat somatosensory cortex to populate the cortex. These me-types were well characterized morphologically and electrophysiologically but they lacked molecular marker identity labels. To extrapolate this missing information, we employed an additional dataset from the Allen Institute for Brain Science containing molecular identity as well as morphological and electrophysiological data for mouse cortical neurons. We first built a latent space based on a number of comparable morphological and electrical features common to both data sources. We then identified 13 morpho-electrical clusters that merged neurons from both datasets while being molecularly homogeneous. The resulting clusters best mirror the molecular identity classification solely using available morpho-electrical features. Finally, we stochastically assigned a molecular identity to a me-type neuron based on the latent space cluster it was assigned to. The resulting mapping was used to derive inhibitory me-types densities in the cortex.Author SummaryThe computational abilities of the brain arise from its organisation principles at the cellular level. One of these principles is the neuronal type composition over different regions. Since computational functions of neurons are best described by their morphological and electrophysiological properties, it is logical to use morpho-electrically defined cell types to describe brain composition. However, characterizing morpho-electrical properties of cells involve low-throughput techniques not very well suited to scan the whole brain. Thanks to recent progress on transcriptomic and immuno-staining techniques we are now able to get a more accurate snapshot of the mouse brain composition for molecularly defined cell types.How to link molecularly defined cell types with morpho-electrical cell types remains an open question. Several studies have explored this problem providing valuable three-modal datasets combining electrical, morphological and molecular properties of cortical neurons. The long-term goal of the Blue Brain Project (BBP) is to accurately model the mouse’s whole brain, which requires detailed biophysical models of neurons. Instead of going through the time-consuming process of producing detailed models from the three-modal datasets, we explored a time-saving method. We mapped the already available detailed morpho-electrical models from the BBP rat dataset to cells from a three-modal mouse dataset. We thus assigned a molecular identity to the neuron models allowing us to populate the whole mouse cortex with detailed neuron models.

The mouse brain contains a rich diversity of inhibitory interneuron types that have been characterized by their patterns of gene expression. However, the distribution of these cell types across the mouse brain is still incomplete. We developed a computational method to establish a consensus on the estimate of the densities of different interneuron types across the mouse brain. This method allows the unbiased integration of diverse and disparate datasets into a framework to predict interneuron densities for uncharted brain regions. We constrained our estimates based on previously computed brain-wide neuron density data, gene expression from in situ hybridization image stacks together with a wide range of values reported in the literature. Using optimization, we derived coherent estimates of cell densities for the different interneuron types. We estimated that 20.3% of all neurons in the mouse brain are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. Our pipeline is extensible, allowing new cell types or data to be integrated as they become available. The data, algorithms, software, and results of this pipeline are publicly available and update the Blue Brain Cell Atlas. We find that our density estimations improve as more literature values are integrated. This work therefore leverages the research community to collectively converge on the numbers of each cell type in each brain region.Author summaryObtaining a global understanding of the cellular composition of the brain is a very complex task, not only because of the great variability that exists between reports of similar counts but also because of the numerous brain regions and cell types that make up the brain. Previously, we presented a model of a cell atlas, which provided an estimate of the densities of neurons, glia and their subtypes for each region in the mouse brain. Here, we describe an extension of this model to include more inhibitory neuron types. We collected estimates of inhibitory neuron counts from literature and built a framework to combine them into a consistent cell atlas. Using brain slice images, we also estimated inhibitory neuron density in regions where no literature data are available. We estimated that in the mouse brain 20.3% of all neurons are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population Our approach can be further extended to any other cell type and provides a resource to build tissue-level models of the rodent brain.