L. Niels Cornelisse

Doc2b is a High Affinity Ca2+ Sensor for Spontaneous Neurotransmitter Release

“Spontaneous” Release Trigger Synaptic vesicle release occurs in different phases that can be tightly coupled to action potentials (synchronous), immediately following action potentials (asynchronous), or as stochastic events not triggered by action potentials (spontaneous). The vesicle protein synaptotagmin is thought to act as the Ca2+ sensor in the synchronous phase, but for the other two phases, Ca2+ sensors have not been identified. Groffen et al. (p. 1614, published online 11 February) now show that cytoplasmic proteins known as Doc2 (double C2 domain) proteins are required for spontaneous release. Doc2 proteins promote membrane fusion in response to exceptionally low increases in Ca2+, and are several orders of magnitude more sensitive to Ca2+ than synaptotagmin. Doc2 and synaptotagmin compete for SNARE-complex binding during membrane fusion. A mutation that abolishes the Ca2+ dependence of Doc2b also abolishes the Ca2+ dependence of spontaneous release. Thus, Doc2 is a high-affinity Ca2+ sensor for spontaneous release that competes with synaptotagmin for SNARE complex binding. Spontaneous synaptic vesicle fusion is triggered by soluble proteins that compete with synaptotagmins to induce membrane curvature. Synaptic vesicle fusion in brain synapses occurs in phases that are either tightly coupled to action potentials (synchronous), immediately following action potentials (asynchronous), or as stochastic events in the absence of action potentials (spontaneous). Synaptotagmin-1, -2, and -9 are vesicle-associated Ca2+ sensors for synchronous release. Here we found that double C2 domain (Doc2) proteins act as Ca2+ sensors to trigger spontaneous release. Although Doc2 proteins are cytosolic, they function analogously to synaptotagmin-1 but with a higher Ca2+ sensitivity. Doc2 proteins bound to N-ethylmaleimide–sensitive factor attachment receptor (SNARE) complexes in competition with synaptotagmin-1. Thus, different classes of multiple C2 domain–containing molecules trigger synchronous versus spontaneous fusion, which suggests a general mechanism for synaptic vesicle fusion triggered by the combined actions of SNAREs and multiple C2 domain–containing proteins.

Docking of Secretory Vesicles Is Syntaxin Dependent

Secretory vesicles dock at the plasma membrane before they undergo fusion. Molecular docking mechanisms are poorly defined but believed to be independent of SNARE proteins. Here, we challenged this hypothesis by acute deletion of the target SNARE, syntaxin, in vertebrate neurons and neuroendocrine cells. Deletion resulted in fusion arrest in both systems. No docking defects were observed in synapses, in line with previous observations. However, a drastic reduction in morphologically docked secretory vesicles was observed in chromaffin cells. Syntaxin-deficient chromaffin cells showed a small reduction in total and plasma membrane staining for the docking factor Munc18-1, which appears insufficient to explain the drastic reduction in docking. The sub-membrane cortical actin network was unaffected by syntaxin deletion. These observations expose a docking role for syntaxin in the neuroendocrine system. Additional layers of regulation may have evolved to make syntaxin redundant for docking in highly specialized systems like synaptic active zones.

Munc18-1 expression levels control synapse recovery by regulating readily releasable pool size

Prompt recovery after intense activity is an essential feature of most mammalian synapses. Here we show that synapses with reduced expression of the presynaptic gene munc18-1 suffer from increased depression during intense stimulation at glutamatergic, GABAergic, and neuromuscular synapses. Conversely, munc18-1 overexpression makes these synapses recover faster. Concomitant changes in the readily releasable vesicle pool and its refill kinetics were found. The number of vesicles docked at the active zone and the total number of vesicles per terminal correlated with both munc18-1 expression levels and the size of the releasable vesicle pool. These data show that varying expression of a single gene controls synaptic recovery by modulating the number of docked, release-ready vesicles and thereby replenishment of the secretion capacity.

Functional Gene Group Analysis Reveals a Role of Synaptic Heterotrimeric G Proteins in Cognitive Ability

Although cognitive ability is a highly heritable complex trait, only a few genes have been identified, explaining relatively low proportions of the observed trait variation. This implies that hundreds of genes of small effect may be of importance for cognitive ability. We applied an innovative method in which we tested for the effect of groups of genes defined according to cellular function (functional gene group analysis). Using an initial sample of 627 subjects, this functional gene group analysis detected that synaptic heterotrimeric guanine nucleotide binding proteins (G proteins) play an important role in cognitive ability (PEMP = 1.9 × 10−4). The association with heterotrimeric G proteins was validated in an independent population sample of 1507 subjects. Heterotrimeric G proteins are central relay factors between the activation of plasma membrane receptors by extracellular ligands and the cellular responses that these induce, and they can be considered a point of convergence, or a “signaling bottleneck.” Although alterations in synaptic signaling processes may not be the exclusive explanation for the association of heterotrimeric G proteins with cognitive ability, such alterations may prominently affect the properties of neuronal networks in the brain in such a manner that impaired cognitive ability and lower intelligence are observed. The reported association of synaptic heterotrimeric G proteins with cognitive ability clearly points to a new direction in the study of the genetic basis of cognitive ability.

Automated analysis of neuronal morphology, synapse number and synaptic recruitment

The shape, structure and connectivity of nerve cells are important aspects of neuronal function. Genetic and epigenetic factors that alter neuronal morphology or synaptic localization of pre- and post-synaptic proteins contribute significantly to neuronal output and may underlie clinical states. To assess the impact of individual genes and disease-causing mutations on neuronal morphology, reliable methods are needed. Unfortunately, manual analysis of immuno-fluorescence images of neurons to quantify neuronal shape and synapse number, size and distribution is labor-intensive, time-consuming and subject to human bias and error. We have developed an automated image analysis routine using steerable filters and deconvolutions to automatically analyze dendrite and synapse characteristics in immuno-fluorescence images. Our approach reports dendrite morphology, synapse size and number but also synaptic vesicle density and synaptic accumulation of proteins as a function of distance from the soma as consistent as expert observers while reducing analysis time considerably. In addition, the routine can be used to detect and quantify a wide range of neuronal organelles and is capable of batch analysis of a large number of images enabling high-throughput analysis.

High speed two-photon imaging of calcium dynamics in dendritic spines: consequences for spine calcium kinetics and buffer capacity

Rapid calcium concentration changes in postsynaptic structures are crucial for synaptic plasticity. Thus far, the determinants of postsynaptic calcium dynamics have been studied predominantly based on the decay kinetics of calcium transients. Calcium rise times in spines in response to single action potentials (AP) are almost never measured due to technical limitations, but they could be crucial for synaptic plasticity. With high-speed, precisely-targeted, two-photon point imaging we measured both calcium rise and decay kinetics in spines and secondary dendrites in neocortical pyramidal neurons. We found that both rise and decay kinetics of changes in calcium-indicator fluorescence are about twice as fast in spines. During AP trains, spine calcium changes follow each AP, but not in dendrites. Apart from the higher surface-to-volume ratio (SVR), we observed that neocortical dendritic spines have a markedly smaller endogenous buffer capacity with respect to their parental dendrites. Calcium influx time course and calcium extrusion rate were both in the same range for spines and dendrites when fitted with a dynamic multi-compartment model that included calcium binding kinetics and diffusion. In a subsequent analysis we used this model to investigate which parameters are critical determinants in spine calcium dynamics. The model confirmed the experimental findings: a higher SVR is not sufficient by itself to explain the faster rise time kinetics in spines, but only when paired with a lower buffer capacity in spines. Simulations at zero calcium-dye conditions show that calmodulin is more efficiently activated in spines, which indicates that spine morphology and buffering conditions in neocortical spines favor synaptic plasticity.

Munc13-1 and Munc18-1 together prevent NSF-dependent de-priming of synaptic vesicles

Synaptic transmission requires a stable pool of release-ready (primed) vesicles. Here we show that two molecules involved in SNARE-complex assembly, Munc13-1 and Munc18-1, together stabilize release-ready vesicles by preventing de-priming. Replacing neuronal Munc18-1 by a non-neuronal isoform Munc18-2 (Munc18-1/2SWAP) supports activity-dependent priming, but primed vesicles fall back into a non-releasable state (de-prime) within seconds. Munc13-1 deficiency produces a similar defect. Inhibitors of N-ethylmaleimide sensitive factor (NSF), N-ethylmaleimide (NEM) or interfering peptides, prevent de-priming in munc18-1/2SWAP or munc13-1 null synapses, but not in CAPS-1/2 null, another priming-deficient mutant. NEM rescues synaptic transmission in munc13-1 null and munc18-1/2SWAP synapses, in acute munc13-1 null slices and even partially in munc13-1/2 double null synapses. Together these data indicate that Munc13-1 and Munc18-1, but not CAPS-1/2, stabilize primed synaptic vesicles by preventing NSF-dependent de-priming.

Empirical Bayesian random censoring threshold model improves detection of differentially abundant proteins.

A challenge in proteomics is that many observations are missing with the probability of missingness increasing as abundance decreases. Adjusting for this informative missingness is required to assess accurately which proteins are differentially abundant. We propose an empirical Bayesian random censoring threshold (EBRCT) model that takes the pattern of missingness in account in the identification of differential abundance. We compare our model with four alternatives, one that considers the missing values as missing completely at random (MCAR model), one with a fixed censoring threshold for each protein species (fixed censoring model) and two imputation models, k-nearest neighbors (IKNN) and singular value thresholding (SVTI). We demonstrate that the EBRCT model bests all alternative models when applied to the CPTAC study 6 benchmark data set. The model is applicable to any label-free peptide or protein quantification pipeline and is provided as an R script.

Automated analysis of secretory vesicle distribution at the ultrastructural level

Neuroendocrine cells like chromaffin cells and PC-12 cells are established models for transport, docking and secretion of secretory vesicles. In micrographs, these vesicles are recognized by their electron dense core. The analysis of secretory vesicle distribution is usually performed manually, which is labour-intensive and subject to human bias and error. We have developed an algorithm to analyze secretory vesicle distribution and docking in electron micrographs. Our algorithm automatically detects the vesicles and calculates their distance to the plasma membrane on basis of the pixel coordinates, ensuring that all vesicles are counted and the shortest distance is measured. We validated the algorithm on a several preparations of endocrine cells. The algorithm was highly accurate in recognizing secretory vesicles and calculating their distribution including vesicle-docking analysis. Furthermore, the algorithm enabled the extraction of parameters that cannot be measured manually like vesicle clustering. Taking together, the algorithm facilitates and expands the unbiased and efficient analysis of secretory vesicle distribution and docking.

Corrigendum to “Automated analysis of neuronal morphology, synapse number and synaptic recruitment” [J. Neurosci. Methods 195 (2) (2011) 185–193]

orrigendum orrigendum to “Automated analysis of neuronal morphology, synapse number nd synaptic recruitment” [J. Neurosci. Methods 195 (2) (2011) 185–193] abine K. Schmitza,1 , J.J. Johannes Hjorthb,1 , Raoul M.S. Joemaic , Rick Wijntjesa , Susanne Eijgenraama, etra de Bruijna, Christina Georgioua, Arthur P.H. de Jonga, Arjen van Ooyenb, Matthijs Verhagea, . Niels Cornelissea,1, Ruud F. Toonena,∗,1, Wouter J.H. Veldkampc,1