Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster.

High-resolution electron microscopy of nervous systems has enabled the reconstruction of synaptic connectomes. However, we do not know the synaptic sign for each connection (i.e., whether a connection is excitatory or inhibitory), which is implied by the released transmitter. We demonstrate that artificial neural networks can predict transmitter types for presynapses from electron micrographs: a network trained to predict six transmitters (acetylcholine, glutamate, GABA, serotonin, dopamine, octopamine) achieves an accuracy of 87% for individual synapses, 94% for neurons, and 91% for known cell types across a D. melanogaster whole brain. We visualize the ultrastructural features used for prediction, discovering subtle but significant differences between transmitter phenotypes. We also analyze transmitter distributions across the brain and find that neurons that develop together largely express only one fast-acting transmitter (acetylcholine, glutamate, or GABA). We hope that our publicly available predictions act as an accelerant for neuroscientific hypothesis generation for the fly.

Network Statistics of the Whole-Brain Connectome of Drosophila.

Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

From connectome to effectome: learning the causal interaction map of the fly brain.

A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons-thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.

Neuronal "parts list" and wiring diagram for a visual system.

A catalog of neuronal cell types has often been called a "parts list" of the brain, and regarded as a prerequisite for understanding brain function. In the optic lobe of Drosophila, rules of connectivity between cell types have already proven essential for understanding fly vision. Here we analyze the fly connectome to complete the list of cell types intrinsic to the optic lobe, as well as the rules governing their connectivity. We more than double the list of known types. Most new cell types contain between 10 and 100 cells, and integrate information over medium distances in the visual field. Some existing type families (transmedullary, lobula intrinsic, and lobula plate intrinsic) at least double in number of types, with implications for perception of color, motion, and form. We introduce a new family, serpentine medulla intrinsic, which has more types than any other, and three new families of types that span multiple neuropils. We demonstrate self-consistency of our cell types through automatic assignment of cells by distance in high-dimensional feature space, and provide further validation by selection of small subsets of discriminative features. Our work showcases the advantages of connectomic cell typing: complete and unbiased sampling, a rich array of features based on connectivity, and reduction of the connectome to a drastically simpler wiring diagram of cell types, with immediate relevance for brain function and development.

CAVE: Connectome Annotation Versioning Engine.

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets (~1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVE's data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila.

The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper 1 , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database 2 . Crucially, this resource includes 4,552 cell types. 3,094 are rigorous consensus validations of cell types previously proposed in the hemibrain connectome 3 . In addition, we propose 1,458 new cell types, arising mostly from the fact that the FlyWire connectome spans the whole brain, whereas the hemibrain derives from a subvolume. Comparison of FlyWire and the hemibrain showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire as the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types proposed in the hemibrain connectome could not yet be reliably identified in the FlyWire connectome. We therefore suggest that cell types should be defined to be robust to inter-individual variation, namely as groups of cells that are quantitatively more similar to cells in a different brain than to any other cell in the same brain. Joint analysis of the FlyWire and hemibrain connectomes demonstrates the viability and utility of this new definition. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.

Neuronal wiring diagram of an adult brain.

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

NEURD: automated proofreading and feature extraction for connectomics.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

Functional connectomics reveals general wiring rule in mouse visual cortex.

To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Binary and analog variation of synapses between cortical pyramidal neurons.

Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 µm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here, we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size. We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.

Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity.

We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 µm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.

FlyWire: online community for whole-brain connectomics.

Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based three-dimensional interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons.

Synapsin Is Required for Dense Core Vesicle Capture and cAMP-Dependent Neuropeptide Release.

Release of neuropeptides from dense core vesicles (DCVs) is essential for neuromodulation. Compared with the release of small neurotransmitters, much less is known about the mechanisms and proteins contributing to neuropeptide release. By optogenetics, behavioral analysis, electrophysiology, electron microscopy, and live imaging, we show that synapsin SNN-1 is required for cAMP-dependent neuropeptide release in Caenorhabditis elegans hermaphrodite cholinergic motor neurons. In synapsin mutants, behaviors induced by the photoactivated adenylyl cyclase bPAC, which we previously showed to depend on ACh and neuropeptides (Steuer Costa et al., 2017), are altered as in animals with reduced cAMP. Synapsin mutants have slight alterations in synaptic vesicle (SV) distribution; however, a defect in SV mobilization was apparent after channelrhodopsin-based photostimulation. DCVs were largely affected in snn-1 mutants: DCVs were ∼30% reduced in synaptic terminals, and their contents not released following bPAC stimulation. Imaging axonal DCV trafficking, also in genome-engineered mutants in the serine-9 protein kinase A phosphorylation site, showed that synapsin captures DCVs at synapses, making them available for release. SNN-1 colocalized with immobile, captured DCVs. In synapsin deletion mutants, DCVs were more mobile and less likely to be caught at release sites, and in nonphosphorylatable SNN-1B(S9A) mutants, DCVs traffic less and accumulate, likely by enhanced SNN-1 dependent tethering. Our work establishes synapsin as a key mediator of neuropeptide release.SIGNIFICANCE STATEMENT Little is known about mechanisms that regulate how neuropeptide-containing dense core vesicles (DCVs) traffic along the axon, how neuropeptide release is orchestrated, and where it occurs. We found that one of the longest known synaptic proteins, required for the regulation of synaptic vesicles and their storage in nerve terminals, synapsin, is also essential for neuropeptide release. By electrophysiology, imaging, and electron microscopy in Caenorhabditis elegans, we show that synapsin regulates this process by tethering the DCVs to the cytoskeleton in axonal regions where neuropeptides are to be released. Without synapsin, DCVs cannot be captured at the release sites and, consequently, cannot fuse with the membrane, and neuropeptides are not released. We suggest that synapsin fulfills this role also in vertebrates, including humans.

Endophilin A and B Join Forces With Clathrin to Mediate Synaptic Vesicle Recycling in Caenorhabditis elegans.

Synaptic vesicle (SV) recycling enables ongoing transmitter release, even during prolonged activity. SV membrane and proteins are retrieved by ultrafast endocytosis and new SVs are formed from synaptic endosomes (large vesicles-LVs). Many proteins contribute to SV recycling, e.g., endophilin, synaptojanin, dynamin and clathrin, while the site of action of these proteins (at the plasma membrane (PM) vs. at the endosomal membrane) is only partially understood. Here, we investigated the roles of endophilin A (UNC-57), endophilin-related protein (ERP-1, homologous to human endophilin B1) and of clathrin, in SV recycling at the cholinergic neuromuscular junction (NMJ) of C. elegans. erp-1 mutants exhibited reduced transmission and a progressive reduction in optogenetically evoked muscle contraction, indicative of impaired SV recycling. This was confirmed by electrophysiology, where particularly endophilin A (UNC-57), but also endophilin B (ERP-1) mutants exhibited reduced transmission. By optogenetic and electrophysiological analysis, phenotypes in the unc-57; erp-1 double mutant are largely dominated by the unc-57 mutation, arguing for partially redundant functions of endophilins A and B, but also hinting at a back-up mechanism for neuronal endocytosis. By electron microscopy (EM), we observed that unc-57 and erp-1; unc-57 double mutants showed increased numbers of synaptic endosomes of large size, assigning a role for both proteins at the endosome, because endosomal disintegration into new SVs, but not formation of endosomes were hampered. Accordingly, only low amounts of SVs were present. Also erp-1 mutants show reduced SV numbers (but no increase in LVs), thus ERP-1 contributes to SV formation. We analyzed temperature-sensitive mutants of clathrin heavy chain (chc-1), as well as erp-1; chc-1 and unc-57; chc-1 double mutants. SV recycling phenotypes were obvious from optogenetic stimulation experiments. By EM, chc-1 mutants showed formation of numerous and large endosomes, arguing that clathrin, as shown for mammalian synapses, acts at the endosome in formation of new SVs. Without endophilins, clathrin formed endosomes at the PM, while endophilins A and B compensated for the loss of clathrin at the PM, under conditions of high SV turnover.

Fast cAMP Modulation of Neurotransmission via Neuropeptide Signals and Vesicle Loading.

Cyclic AMP (cAMP) signaling augments synaptic transmission, but because many targets of cAMP and protein kinase A (PKA) may be involved, mechanisms underlying this pathway remain unclear. To probe this mechanism, we used optogenetic stimulation of cAMP signaling by Beggiatoa-photoactivated adenylyl cyclase (bPAC) in Caenorhabditis elegans motor neurons. Behavioral, electron microscopy (EM), and electrophysiology analyses revealed cAMP effects on both the rate and on quantal size of transmitter release and led to the identification of a neuropeptidergic pathway affecting quantal size. cAMP enhanced synaptic vesicle (SV) fusion by increasing mobilization and docking/priming. cAMP further evoked dense core vesicle (DCV) release of neuropeptides, in contrast to channelrhodopsin (ChR2) stimulation. cAMP-evoked DCV release required UNC-31/Ca2+-dependent activator protein for secretion (CAPS). Thus, DCVs accumulated in unc-31 mutant synapses. bPAC-induced neuropeptide signaling acts presynaptically to enhance vAChT-dependent SV loading with acetylcholine, thus causing increased miniature postsynaptic current amplitudes (mPSCs) and significantly enlarged SVs.

High-Throughput All-Optical Analysis of Synaptic Transmission and Synaptic Vesicle Recycling in Caenorhabditis elegans.

Synaptic vesicles (SVs) undergo a cycle of biogenesis and membrane fusion to release transmitter, followed by recycling. How exocytosis and endocytosis are coupled is intensively investigated. We describe an all-optical method for identification of neurotransmission genes that can directly distinguish SV recycling factors in C. elegans, by motoneuron photostimulation and muscular RCaMP Ca2+ imaging. We verified our approach on mutants affecting synaptic transmission. Mutation of genes affecting SV recycling (unc-26 synaptojanin, unc-41 stonin, unc-57 endophilin, itsn-1 intersectin, snt-1 synaptotagmin) showed a distinct 'signature' of muscle Ca2+ dynamics, induced by cholinergic motoneuron photostimulation, i.e. faster rise, and earlier decrease of the signal, reflecting increased synaptic fatigue during ongoing photostimulation. To facilitate high throughput, we measured (3-5 times) ~1000 nematodes for each gene. We explored if this method enables RNAi screening for SV recycling genes. Previous screens for synaptic function genes, based on behavioral or pharmacological assays, allowed no distinction of the stage of the SV cycle in which a protein might act. We generated a strain enabling RNAi specifically only in cholinergic neurons, thus resulting in healthier animals and avoiding lethal phenotypes resulting from knockdown elsewhere. RNAi of control genes resulted in Ca2+ measurements that were consistent with results obtained in the respective genomic mutants, albeit to a weaker extent in most cases, and could further be confirmed by opto-electrophysiological measurements for mutants of some of the genes, including synaptojanin. We screened 95 genes that were previously implicated in cholinergic transmission, and several controls. We identified genes that clustered together with known SV recycling genes, exhibiting a similar signature of their Ca2+ dynamics. Five of these genes (C27B7.7, erp-1, inx-8, inx-10, spp-10) were further assessed in respective genomic mutants; however, while all showed electrophysiological phenotypes indicative of reduced cholinergic transmission, no obvious SV recycling phenotypes could be uncovered for these genes.

A novel CaM kinase II pathway controls the location of neuropeptide release from Caenorhabditis elegans motor neurons.

Neurons release neuropeptides via the regulated exocytosis of dense core vesicles (DCVs) to evoke or modulate behaviors. We found that Caenorhabditis elegans motor neurons send most of their DCVs to axons, leaving very few in the cell somas. How neurons maintain this skewed distribution and the extent to which it can be altered to control DCV numbers in axons or to drive release from somas for different behavioral impacts is unknown. Using a forward genetic screen, we identified loss-of-function mutations in UNC-43 (CaM kinase II) that reduce axonal DCV levels by ∼90% and cell soma/dendrite DCV levels by ∼80%, leaving small synaptic vesicles largely unaffected. Blocking regulated secretion in unc-43 mutants restored near wild-type axonal levels of DCVs. Time-lapse video microscopy showed no role for CaM kinase II in the transport of DCVs from cell somas to axons. In vivo secretion assays revealed that much of the missing neuropeptide in unc-43 mutants is secreted via a regulated secretory pathway requiring UNC-31 (CAPS) and UNC-18 (nSec1). DCV cargo levels in unc-43 mutants are similarly low in cell somas and the axon initial segment, indicating that the secretion occurs prior to axonal transport. Genetic pathway analysis suggests that abnormal neuropeptide function contributes to the sluggish basal locomotion rate of unc-43 mutants. These results reveal a novel pathway controlling the location of DCV exocytosis and describe a major new function for CaM kinase II.

An organelle gatekeeper function for Caenorhabditis elegans UNC-16 (JIP3) at the axon initial segment.

Neurons must cope with extreme membrane trafficking demands to produce axons with organelle compositions that differ dramatically from those of the cell soma and dendrites; however, the mechanism by which they accomplish this is not understood. Here we use electron microscopy and quantitative imaging of tagged organelles to show that Caenorhabditis elegans axons lacking UNC-16 (JIP3/Sunday Driver) accumulate Golgi, endosomes, and lysosomes at levels up to 10-fold higher than wild type, while ER membranes are largely unaffected. Time lapse microscopy of tagged lysosomes in living animals and an analysis of lysosome distributions in various regions of unc-16 mutant axons revealed that UNC-16 inhibits organelles from escaping the axon initial segment (AIS) and moving to the distal synaptic part of the axon. Immunostaining of native UNC-16 in C. elegans neurons revealed a localized concentration of UNC-16 at the initial segment, although UNC-16 is also sparsely distributed in distal regions of axons, including the synaptic region. Organelles that escape the AIS in unc-16 mutants show bidirectional active transport within the axon commissure that occasionally deposits them in the synaptic region, where their mobility decreases and they accumulate. These results argue against the long-standing, untested hypothesis that JIP3/Sunday Driver promotes anterograde organelle transport in axons and instead suggest an organelle gatekeeper model in which UNC-16 (JIP3/Sunday Driver) selectively inhibits the escape of Golgi and endosomal organelles from the AIS. This is the first evidence for an organelle gatekeeper function at the AIS, which could provide a regulatory node for controlling axon organelle composition.

Differential roles for snapin and synaptotagmin in the synaptic vesicle cycle.

Evoked synaptic transmission is dependent on interactions between the calcium sensor Synaptotagmin I and the SNARE complex, comprised of Syntaxin, SNAP-25, and Synaptobrevin. Recent evidence suggests that Snapin may be an important intermediate in this process, through simultaneous interactions of Snapin dimers with SNAP-25 and Synaptotagmin. In support of this model, cultured neurons derived from embryonically lethal Snapin null mutant mice exhibit desynchronized release and a reduced readily releasable vesicle pool. Based on evidence that a dimerization-defective Snapin mutation specifically disrupts priming, Snapin is hypothesized to stabilize primed vesicles by structurally coupling Synaptotagmin and SNAP-25. To explore this model in vivo we examined synaptic transmission in viable, adult C. elegans Snapin (snpn-1) mutants. The kinetics of synaptic transmission were unaffected at snpn-1 mutant neuromuscular junctions (NMJs), but the number of docked, fusion competent vesicles was significantly reduced. However, analyses of snt-1 and snt-1;snpn-1 double mutants suggest that the docking role of SNPN-1 is independent of Synaptotagmin. Based on these results we propose that the primary role of Snapin in C. elegans is to promote vesicle priming, consistent with the stabilization of SNARE complex formation through established interactions with SNAP-25 upstream of the actions of Synaptotagmin in calcium-sensing and endocytosis.

Competition between high- and higher-mutating strains of Escherichia coli.

Experimental studies have shown that a mutator allele can readily hitchhike to fixation with beneficial mutations in an asexual population having a low, wild-type mutation rate. Here, we show that a genotype bearing two mutator alleles can supplant a population already fixed for one mutator allele. Our results provide experimental support for recent theory predicting that mutator alleles will tend to accumulate in asexual populations by hitchhiking with beneficial mutations, causing an ever-higher genomic mutation rate.