Neuronal dendrites must relay synaptic inputs over long distances, but the mechanisms by which activity-evoked intracellular signals propagate over macroscopic distances remain unclear. Here, we discovered a system of periodically arranged endoplasmic reticulum-plasma membrane (ER-PM) junctions tiling the plasma membrane of dendrites at \~1 μm intervals, interlinked by a meshwork of ER tubules patterned in a ladder-like array. Populated with Junctophilin-linked plasma membrane voltage-gated Ca2+ channels and ER Ca2+-release channels (ryanodine receptors), ER-PM junctions are hubs for ER-PM crosstalk, fine-tuning of Ca2+ homeostasis, and local activation of the Ca2+/calmodulin-dependent protein kinase II. Local spine stimulation activates the Ca2+ modulatory machinery facilitating voltage-independent signal transmission and ryanodine receptor-dependent Ca2+ release at ER-PM junctions over 20 μm away. Thus, interconnected ER-PM junctions support signal propagation and Ca2+ release from the spine-adjacent ER. The capacity of this subcellular architecture to modify both local and distant membrane-proximal biochemistry potentially contributes to dendritic computations.HighlightsPeriodic ER-PM junctions tile neuronal dendritic plasma membrane in rodent and fly.ER-PM junctions are populated by ER tethering and Ca2+ release and influx machinery.ER-PM junctions act as sites for local activation of CaMKII.Local spine activation drives Ca2+ release from RyRs at ER-PM junctions over 20 μm.

Primary cilia on granule cell neuron progenitors in the developing cerebellum detect sonic hedgehog to facilitate proliferation. Following differentiation, cerebellar granule cells become the most abundant neuronal cell type in the brain. While essential during early developmental stages, the fate of granule cell cilia is unknown. Here, we provide nanoscopic resolution of ciliary dynamics by studying developmental changes in granule cell cilia using large-scale electron microscopy volumes and immunostaining of mouse cerebella. We found that many granule cell primary cilia were intracellular and concealed from the external environment. Cilia were disassembed in differentiating granule cell neurons in a process we call cilia deconstruction that was distinct from pre-mitotic cilia resorption in proliferating progenitors. In differentiating granule cells, ciliary loss involved unique disassembly intermediates, and, as maturation progressed, mother centriolar docking at the plasma membrane. Cilia did not reform from the docked centrioles, rather, in adult mice granule cell neurons remained unciliated. Many neurons in other brain regions require cilia to regulate function and connectivity. In contrast, our results show that granule cell progenitors had concealed cilia that underwent deconstruction potentially to prevent mitogenic hedgehog responsiveness. The ciliary deconstruction mechanism we describe could be paradigmatic of cilia removal during differentiation in other tissues.

Persistent changes in spine shape are coupled to long-lasting synaptic plasticity in hippocampus. The molecules that coordinate such persistent structural and functional plasticity are unknown. Here, we generated mice in which the cell adhesion molecule N-cadherin was conditionally ablated from postnatal, excitatory synapses in hippocampus. We applied to adult mice of either sex a combination of whole-cell recording, two-photon microscopy, and spine morphometric analysis to show that postnatal ablation of N-cadherin has profound effects on the stability of coordinated spine enlargement and long-term potentiation (LTP) at mature CA1 synapses, with no effects on baseline spine density or morphology, baseline properties of synaptic neurotransmission, or long-term depression. Thus, N-cadherin couples persistent spine structural modifications with long-lasting synaptic functional modifications associated selectively with LTP, revealing unexpectedly distinct roles at mature synapses in comparison with earlier, broader functions in synapse and spine development.

Recurrent connections are thought to be a common feature of the neural circuits that encode memories, but how memories are laid down in such circuits is not fully understood. Here we present evidence that courtship memory in Drosophila relies on the recurrent circuit between mushroom body gamma (MBg), M6 output, and aSP13 dopaminergic neurons. We demonstrate persistent neuronal activity of aSP13 neurons and show that it transiently potentiates synaptic transmission from MBγ>M6 neurons. M6 neurons in turn provide input to aSP13 neurons, prolonging potentiation of MBγ>M6 synapses over time periods that match short-term memory. These data support a model in which persistent aSP13 activity within a recurrent circuit lays the foundation for a short-term memory.

The mammalian hippocampus forms a cognitive map using neurons that fire according to an animal's position ("place cells") and many other behavioral and cognitive variables. The responses of these neurons are shaped by their presynaptic inputs and the nature of their postsynaptic integration. In CA1 pyramidal neurons, spatial responses in vivo exhibit a strikingly supralinear dependence on baseline membrane potential. The biophysical mechanisms underlying this nonlinear cellular computation are unknown. Here, through a combination of in vitro, in vivo, and in silico approaches, we show that persistent sodium current mediates the strong membrane potential dependence of place cell activity. This current operates at membrane potentials below the action potential threshold and over seconds-long timescales, mediating a powerful and rapidly reversible amplification of synaptic responses, which drives place cell firing. Thus, we identify a biophysical mechanism that shapes the coding properties of neurons composing the hippocampal cognitive map.

Auditory feedback is critical for the development and maintenance of speech in humans. In contrast, studies of nonhuman primate vocal production generally report that subjects show little reliance on auditory input. We examined the extent to which cotton-top tamarin (Saguinus oedipus) vocal production is sensitive to perturbation of auditory feedback by manipulating the predictability of presentation of a 1 s burst of white noise during the production of the species-specific contact call, the combination long call (CLC). We used three experimental conditions: the Begin condition, in which white noise was presented only during the first half of a recording session, the End condition, in which white noise was presented only in the last half, and the Random condition, in which each call had a 50% probability of receiving white noise playback throughout the recording session, making the auditory feedback unpredictable. In addition we recorded calls before and after the experimental series (Baseline condition) to determine whether any changes induced by modification of auditory feedback persisted. Results showed that playback of white noise during the production of the CLC produced changes in the temporal structure of the CLC: calls were shorter and had fewer pulses, indicating that modification of auditory feedback can interrupt vocal production. In addition, calls that received modified feedback were louder and had longer inter-pulse intervals than those that did not, consistent with an adaptive response to the masking effect of white noise playback. The magnitude of this compensatory effect and the interruption rate were both sensitive to whether the feedback modification occurred at the beginning or end of the experimental session: early feedback produced less interruption and more compensation. Finally, when auditory feedback modification was unpredictable, adaptive changes were observed in both calls that received modified feedback and those that received normal feedback, suggesting that tamarins can generate an expectation of noise playback and increase vocal amplitude in anticipation of masking.

Fold-switching proteins challenge the one-sequence-one-structure paradigm by adopting multiple stable folds. Nevertheless, it is uncertain whether fold switchers are naturally pervasive or rare exceptions to the well-established rule. To address this question, we developed a predictive method and applied it to the NusG superfamily of >15,000 transcription factors. We predicted that a substantial population (25%) of the proteins in this family switch folds. Circular dichroism and nuclear magnetic resonance spectroscopies of 10 sequence-diverse variants confirmed our predictions. Thus, we leveraged family-wide predictions to determine both conserved contacts and taxonomic distributions of fold-switching proteins. Our results indicate that fold switching is pervasive in the NusG superfamily and that the single-fold paradigm significantly biases structure-prediction strategies.

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

Pfam, available via servers in the UK (http://pfam.sanger.ac.uk/) and the USA (http://pfam.janelia.org/), is a widely used database of protein families, containing 14 831 manually curated entries in the current release, version 27.0. Since the last update article 2 years ago, we have generated 1182 new families and maintained sequence coverage of the UniProt Knowledgebase (UniProtKB) at nearly 80%, despite a 50% increase in the size of the underlying sequence database. Since our 2012 article describing Pfam, we have also undertaken a comprehensive review of the features that are provided by Pfam over and above the basic family data. For each feature, we determined the relevance, computational burden, usage statistics and the functionality of the feature in a website context. As a consequence of this review, we have removed some features, enhanced others and developed new ones to meet the changing demands of computational biology. Here, we describe the changes to Pfam content. Notably, we now provide family alignments based on four different representative proteome sequence data sets and a new interactive DNA search interface. We also discuss the mapping between Pfam and known 3D structures.