, Birinapant purchase 2004), but their genesis is unknown. Connecting such morphological phenotypes, as well as the basic developmental mechanisms controlling production, migration, and areal allocation of neurons, to genetic adaptations that have occurred in the anthropoid primate and human lineages is the next critical step if we are to understand human cortical evolution. It is clearly not a one-way process, as genetic distinctions can be used to guide phenotype discovery. These genetic factors are addressed in the following sections. Comparative genomics provides a powerful

platform for identifying the genes and adaptive regulatory changes involved in cerebral cortical expansion, arealization, and other human-specific cellular or connectivity phenotypes (e.g., Table 1; Li et al., 2013 and Rilling et al., 2008). The basic assumption underlying this paradigm is that changes in the genome on the human lineage, whether individual nucleotides, insertion-deletions (indels), or larger structural chromosomal variation, underlie the

basic developmental processes described above. By comparing the human sequence to other mammals, one can infer that common DNA sequences represent those of the common ancestor and that those that differ between the two represent changes occurring in either species. Critical to interpretation of these data is comparison selleck compound to another species that is a common but more distantly related ancestor, called an outgroup,

without which understanding whether the observed differences occur on the human lineage is not possible (reviewed in Preuss et al., 2004 and Varki and Altheide, 2005). Many forms of genetic variation that distinguish human from other species have been identified (reviewed in O’Bleness et al., 2012, Scally et al., 2012 and Varki et al., 2008). The process of identifying variation is framed by the daunting prospect of sifting through tens of millions of base pairs that differ between humans and their closest relatives to identify those that are most divergent. 4-Aminobutyrate aminotransferase Once such variants are found, connecting them to specific tissues, such as the brain, and, within the brain, to specific phenotypes, poses additional challenges. Thus, it should not be surprising that few clear smoking guns have been identified that distinguish the human brain from that of other species, including anthropoid primates. It is estimated that single-nucleotide differences, indels, and structural chromosomal changes comprising about 4% of the genome differ between humans and chimpanzees, providing a finite space for exploring the differences between ourselves and our closest living ancestor (Cheng et al., 2005, Prado-Martinez et al., 2013, Prüfer et al., 2012 and Sudmant et al., 2013).

We have previously observed that recombinant tau fibrils will induce aggregation of full-length intracellular tau in cultured cells and that aggregated forms of tau transfer between cells (Frost et al., 2009). Further, we found that intracellular tau fibrils are released free into the media, where they propagate aggregation by direct interaction

with native tau in recipient cells. An anti-tau antibody (HJ9.3) blocks this process by preventing Olaparib molecular weight tau aggregate uptake into recipient cells (Kfoury et al., 2012). In addition to similar experiments with recombinant tau (Guo and Lee, 2011), others have shown that paired helical filaments from AD brain induce cytoplasmic tau aggregation (Santa-Maria et al., 2012). Injection of brain extract from human P301S

tau transgenic mice into the brains of mice expressing wild-type human tau induces assembly of wild-type human tau into filaments and spreading of pathology (Clavaguera et al., 2009). Similar effects occurred after injection of recombinant full-length or truncated tau fibrils, which caused rapid induction of NFT-like inclusions that propagated from injected sites to connected brain regions in a time-dependent manner (Iba et al., 2013). Selective tau expression in the entorhinal cortex caused late pathology in the axonal terminal zones in cells in the dentate gyrus and hippocampus, consistent with transsynaptic movement of aggregates (de Calignon et al.,

2012 and Liu et al., 2012). A growing body NVP-AUY922 in vitro of work thus supports the idea that tau aggregates transfer between cells and might be targeted with therapeutic antibodies. In mouse models that mimic aspects of AD and Parkinson’s disease, passive immunization using antibodies against Aβ and alpha synuclein can reduce Aβ and alpha-synuclein deposition in brain (Bard et al., 2000, DeMattos et al., 2001 and Masliah et al., 2011) and improve behavioral deficits (Dodart et al., 2002, Kotilinek et al., 2002 and Masliah et al., 2011). Active immunization in tauopathy mouse models using tau phospho peptides reduced tau pathology (Bi et al., 2011 and Boimel et al., 2010) and in some studies improved behavioral deficits 17-DMAG (Alvespimycin) HCl (Asuni et al., 2007, Boutajangout et al., 2010 and Troquier et al., 2012). In two passive vaccination studies, there was reduced tau pathology and improved motor function when the antibody was given prior to the onset of pathology (Boutajangout et al., 2011 and Chai et al., 2011). While several of the tau immunization studies appear to show some beneficial effects, the maximal expected efficacy of anti-tau antibodies administered after the onset of pathology, the optimal tau species to target, and the mechanism of the therapeutic effect have remained unknown. Our prior work in cell culture has suggested that aggregate flux in and out of cells might be central to progressive pathology (Kfoury et al., 2012).

Different sensilla responded to different subsets of stimuli. For example, I9 and I10 responded strongly to theophylline (TPH) but not DEN, whereas I4 and I5 responded

strongly to DEN but not TPH (Figure 1D). Inspection of the response matrix (Figure 3) reveals extensive heterogeneity among the labellar sensilla, and by extension, among the bitter neurons that they contain. The L sensilla exhibited little or no physiological response to our panel of tastants, in agreement with a previous report (Hiroi et al., BMS-354825 solubility dmso 2004). Two of the S sensilla, S4 and S8, also did not respond to any bitter tastants. All other S type sensilla were broadly tuned, responding to 9–15 of the 16 compounds with a spike frequency of ≥10 spikes/s

(Figure 3, Tables S1 and S2). I type sensilla were more narrowly tuned with respect to our panel of tastants, responding to 3–7 compounds. The strongest response was elicited by 10 mM CAF in the S5 sensillum (60.8 ± 3.3 spikes/s; n = 34). A hierarchical clustering analysis identified five functional classes of labellar sensilla: two classes of broadly tuned sensilla (S-a and S-b), two classes of narrowly tuned sensilla (I-a and I-b), and a fifth class that did not display excitatory responses to any of our panel of tastants (L, S-c) (Figures 4A and 4B). The two classes of S sensilla are both broadly tuned, but the S-b sensilla exhibit greater mean responses

to most tastants (Figure 4B). Notably, this class comprises the three sensilla that uniquely exhibited a second buy NVP-AUY922 mafosfamide high-frequency action potential (Figure 1C). The more narrowly tuned I-a and I-b sensilla respond to complementary subsets of tastants. Maps of the distribution of the sensilla of each class are shown in Figure 4C. The most broadly tuned sensilla (S-a and S-b classes) are located in the medial region of the labellum, while the narrowly tuned sensilla (I-a and I-b classes) are in lateral regions. The three classes of S sensilla are intermingled in the row of medial sensilla, while the I-a and I-b sensilla are restricted to the anterior and posterior portions of the labellum, respectively. We note with interest that among the five bitter compounds that elicited responses >10 spikes/s from the I-a sensilla, three elicited the most aversive behavioral responses (DEN, sparteine sulfate salt [SPS], and (-)- lobeline hydrochloride [LOB]), and one elicited the fifth most aversive response (berberine chloride [BER]) (Figure 2C). The median isoattractive concentration for these five tastants was <0.1 mM; the median concentration for all the others was ∼1 mM. Although gustatory input from other organs such as the legs probably influences this behavior, these results suggest the possibility that different classes of bitter-sensing neurons make different contributions to the behavior of the fly.

Electrode impedance was kept below 5 kΩ. EEG was amplified with a gain of 500 K, bandpass filtered at 0.05–100 Hz, and digitized at a sampling rate of 500 Hz. The signals on these electrodes were referenced online to the nose and were rereferenced SCR7 clinical trial offline to the average of two mastoids. Using Brain Vision Analyzer (Brain Products, Munich, Germany), eye-blink

artifacts were semi-automatically corrected using the procedure described by Gratton et al. (1983). EEG epochs lasting 350 ms, starting at 100 ms before the texture stimulus onset, were made. They were selectively averaged according to the orientation contrast. Epochs with EEG or residual EOG exceeding ±50 μV at any electrode were excluded from the average. The average waveforms were low-pass filtered at 40 Hz and baseline corrected with respect to the average voltage during the 100-ms prestimulus interval. The C1 response was apparent between 60 and 90 ms after stimulus onset. To select electrodes for the C1 amplitude and latency analysis, grand averaged ERPs were

made by averaging across subjects and orientation contrasts. Posterior electrodes, including CP1, CPz, CP2, P1, Pz, and P2, had the largest C1 amplitudes. To quantify the C1 amplitude for each subject, www.selleckchem.com/products/Dasatinib.html the mean amplitude of the five sampling points around the C1 peak was first calculated for each of these six electrodes, and this mean was then averaged across the six electrodes. The C1 latency was the mean of the peak latencies across these six electrodes. Estimation of the dipole sources was performed using the BESA algorithm as described by Clark and Hillyard (1996) and Frey et al. (2010). The C1 component was modeled based jointly on the grand-averaged waveforms elicited by texture stimuli with the four orientation contrasts. The waveform in the interval between 62 and 82 ms was simulated with two dipoles, one in each hemisphere, which were constrained to have mirror-symmetrical locations, but allowed to vary in orientation. The initial

starting positions of dipoles were randomly chosen and using different starting locations yielded high similar dipole configurations. The event-related fMRI experiment consisted of 17-DMAG (Alvespimycin) HCl four functional scans of 128 continuous trials. Each scan began with 6 s fixation and lasted 274 s. There were four types of trials—orientation contrast trials (7.5°, 15°, and 90°) and fixation trial. In an orientation contrast trial, a texture stimulus was presented for 50 ms, followed by a 100 ms mask and 1,850 ms fixation. Similar to the 2AFC experiment, subjects were asked to indicate the location of the foreground region, which was left to the fixation in one half of orientation contrast trials and right in the other half at random. In a fixation trial, only the fixation point was presented for 2 s. In a scan, there were 32 trials for each type of trial.

Because CYFIP1 is abundant at synapses (Figure S1), we immunoprecipitated CYFIP1 from mouse cortical synaptoneurosomes. Sixteen proteins were identified by MS, seven of which had not been detected in the cortical lysate data set likely because they are enriched in the CYFIP1 complexes in the synaptic compartment. The synaptic CYFIP1 interactome contained not only RBPs, but also cytoskeleton-related proteins, including components of the WRC (NCKAP1, ABI1/2, and WAVE1; Figure 6D; Tables S3 and S4). These results further demonstrate that CYFIP1 is active in regulating http://www.selleckchem.com/products/cx-5461.html mRNA translation and determining cytoskeleton-based cell morphology. Deletions

and duplications of a chromosomal region including CYFIP1 have been linked

CT99021 ic50 to ID, ASD, and schizophrenia ( Cooper et al., 2011, Doornbos et al., 2009, Leblond et al., 2012, Murthy et al., 2007, Nowicki et al., 2007, Tam et al., 2010, van der Zwaag et al., 2010, von der Lippe et al., 2010 and Zhao et al., 2012). We reasoned that proteins in the same protein network might have a similar pathological effect. A literature search on disease involvement of the genes in question revealed that 25 out of the 40 proteins that bind CYFIP1 are encoded by genes associated with ID, ASD, ADHD, schizophrenia, major depressive disorder, and Alzheimer’s disease ( Tables S4 and S5). In addition, a gene-based analysis interrogating for association with schizophrenia based on the meta-analytic p values obtained by the largest schizophrenia Farnesyltransferase genome-wide association study to date (

Ripke et al., 2011) (9,394 cases and 12,462 controls) revealed that 8 out of 36 tested autosomal genes of the CYFIP1 interactome had a nominally significant p value (<0.05) for association with schizophrenia ( Tables S4–S6). This significantly exceeds the expectation (1.8 genes) under the null hypothesis of no association (one-sided Fisher’s exact test, p = 0.042), although the polygenic nature of schizophrenia should be considered. One gene, FAM120A, was significantly associated with schizophrenia (p = 0.00064) after Bonferroni correction for testing 36 genes. In summary, 25 proteins out of 40 identified in our CYFIP1 interactome are encoded by genes involved in diseases: 26% are associated with schizophrenia, 19% with ASD, and 10% with ID ( Table S4; Figure 6E). These observations suggest that CYFIP1 and its interaction partners are linked to pathways that, if impaired, can be associated with intellectual disabilities and psychiatric disorders. CYFIP1 is present in two functional complexes essential for synaptic morphology and function: a ribonucleoparticle repressing protein synthesis and the WAVE regulatory complex (Figure 6F). When CYFIP1 interacts with NCKAP1 forming a platform for the assembly of the WRC, the interaction with eIF4E is obstructed and vice versa.

R.), Mayo Foundation and MCF ALS Center donor funds (K.B.B.). R.R. is also funded by NIH grants R01 NS065782 and R01 AG026251. Some TDP-43 analysis was funded by NIH grant R01 AG037491 (K.A.J.). Z.K.W. is partially supported by the NIH/NINDS 1RC2NS070276, NS057567, P50NS072187, Mayo Clinic Florida (MCF) Research Committee CR program MAPK inhibitor (MCF #90052030), Dystonia Medical Research Foundation, and the gift from Carl Edward Bolch, Jr., and Susan Bass Bolch (MCF #90052031/PAU #90052).The UBC studies were funded by the Canadian Institutes of Health Research (CIHR) Operating Grants #179009 and #74580 and by the Pacific Alzheimer’s Research Foundation (PARF) Center Grant C06-01. G-YRH is supported by a Clinical Genetics Investigatorship award

from the CIHR. A.L.B. is funded by R01AG038791, R01AG031278, the John Douglas French Foundation, the Hellman Family Foundation, and the Tau find more Research Consortium. B.L.M. is funded by

P50AG023501, P01AG019724, the Larry Hillblom Foundation, and the State of CA and P50 AG1657303 to B.L.M. and W.W.S. “
“Amyotrophic lateral sclerosis (ALS, OMIM #105400) is a fatal neurodegenerative disease characterized clinically by progressive paralysis leading to death from respiratory failure, typically within two to three years of symptom onset (Rowland and Shneider, 2001). ALS is the third most common neurodegenerative disease in the Western world (Hirtz et al., 2007), and there are currently no effective therapies. Approximately 5% of cases are familial in nature, whereas the bulk of patients diagnosed with the disease are classified as sporadic as they appear to occur randomly throughout the population TCL (Chiò et al., 2008). There is growing recognition, based on clinical, genetic, and epidemiological data, that ALS and frontotemporal dementia (FTD, OMIM #600274) represent an overlapping continuum of disease, characterized pathologically by the presence of TDP-43 positive inclusions throughout the central nervous system (Lillo and Hodges, 2009 and Neumann et al., 2006). To date, a number of genes have been

discovered as causative for classical familial ALS, namely SOD1, TARDBP, FUS, OPTN, and VCP ( Johnson et al., 2010, Kwiatkowski et al., 2009, Maruyama et al., 2010, Rosen et al., 1993, Sreedharan et al., 2008 and Vance et al., 2009). These genes cumulatively account for ∼25% of familial cases, indicating that other causative genes remain to be identified. Each new gene implicated in the etiology of ALS or FTD provides fundamental insights into the cellular mechanisms underlying neuron degeneration, as well as facilitating disease modeling and the design and testing of targeted therapeutics; thus, the identification of new genes that cause ALS or FTD is of great significance. Linkage analysis of kindreds involving multiple cases of ALS, FTD, and ALS-FTD had suggested that there was an important locus for the disease on the short arm of chromosome 9 (Boxer et al., 2011, Morita et al.

, 1992) to rapamycin (200 nM, 3.5 hr). The macroautophagy-related check details protein LC3 exists in two forms, LC3-I and LC3-II, a phosphatidylethanolamine-conjugated form of LC3-I. LC3-I is widely distributed in the cytosol, whereas the conjugated LC3-II form specifically associates with AV membranes (Mizushima et al., 2004). Dopamine neurons were identified by TH immunolabel, and immunolabel for native LC3 was used to identify AVs. There were occasional LC3-immunolabeled puncta in the Atg7-deficient cell bodies and neurites, possibly due to noncanonical AV formation (Nishida et al., 2009). Rapamycin strikingly increased LC3-immunolabeled

puncta in dopamine cell bodies and neurites in DAT Cre mice but had no effect on puncta in DAT Cre Atg7 mutants (p < 0.01; ANOVA) (Figures 3A–3C), showing that induction of AVs by rapamycin required Atg7 expression. We then examined the induction of LC3-II by rapamycin (3 μM) in acute striatal slices by western blotting. Rapamycin at 3.5 hr produced a 56% increase in LC3-II (Figure 3D) (p < HSP inhibitor 0.001; t test), but this response was no longer apparent at 7 hr, indicating that

rapamycin induced a transient increase of LC3-II, a characteristic of macroautophagy. In electron micrographs of striatal slices, we identified AV-like organelles based on previously described criteria (Yu et al., 2004) as nonmitochondrial structures in presynaptic terminals that possessed multiple membranes, usually with luminal content.

almost These organelles were different from multivesicular bodies, organelles of the autophagic-lysosomal pathway that typically displays an even distribution of vesicles in the lumen. Many AV-like organelles contained a wide range of luminal constituents, including small vesicles resembling synaptic vesicles (compare Figures 4A and 4B). Some multilamellar structures were devoid of obvious luminal electron-dense material (Martinez-Vicente et al., 2010), possibly due to acute induction of AVs by rapamycin. It is likely that some of these multilamellar organelles include endosomes or are “amphisomes” that result from fusion of endosomes and AVs. Rapamycin in the striatal slice more than doubled the number of presynaptic terminal profiles containing AV-like structures from 15.4% of control terminal profiles (n = 65) to 35.5% in rapamycin-treated terminals (n = 75; p < 0.05; chi-square test; Figure 4C) and decreased terminal profile areas by 19% (p < 0.05; t test; Figure 4D). Striatal terminal profiles from rapamycin-treated samples, of which only a small fraction are dopaminergic, moreover contained fewer synaptic vesicles than untreated controls (49.2 ± 3.6, n = 75 versus 70.1 ± 4.2, n = 65; p < 0.0001, respectively; t test; Figure 4E). Dopamine axonal varicosities typically do not display presynaptic or postsynaptic densities (Nirenberg et al.

This forgetting measure was based on an associative memory accuracy index in which we corrected for source false alarms by subtracting the proportion of trials of a given condition that were given an incorrect source response from the proportion of trials afforded a correct source response for that condition. Thus, our index of associative forgetting was the following: (associative memory accuracy on test 1 − associative memory accuracy on test 2)/(associative memory accuracy on test 1). Greenhouse-Geisser-corrected degrees GW-572016 cost of freedom are reported

for repeated-measures ANOVAs where appropriate. As expected, associative memory performance decreased across tests, F(1, 23) = 160.6, p < 0.001. Analysis of associative recognition performance on each test separately

revealed main effects of condition (LD, SD, and SS) for both objects and scenes (immediate test: for object trials, F(2, 45.7) = 58.8, p < 0.001, for scene trials, F(1.9, 42.8) = 32.9, p < 0.001; 24 hr test: for object trials, F(2, 45.7) = 63.2, p < 0.001, for scene trials, F(1.9, 44.1) = 32.7, p < 0.001). These effects manifest as better associative recognition for both the LD and SD trials compared to SS trials for both object and scene pairs (for the immediate test, LD versus SS objects: F(1, 23) = 81.4, p < 0.001, SD versus SS objects: F(1, 23) = 98.9, p < 0.001, LD versus SS scenes: F(1, 23) = 54.5, p < 0.001, SD versus SS scenes: F(1, 23) = 44.9, p < 0.001; for the 24 hr test, LD versus SS objects: F(1, 23) = 108.7, p < 0.001, SD versus SS objects: F(1, 23) = 80.5, p < 0.001, LD versus SS scenes: F(1, 23) = 40.3, p < 0.001, click here SD versus SS scenes: F(1, 23) = 54.7, p < 0.001). These results were not surprising given that both LD and SD trials were studied twice, while the SS trials were only studied once. While no differences in associative recognition between object and scene trials were identified on the immediate test, F(1, 23) = 3.2, p > 0.08, on the 24 hr test, scene trials were associated

with better associative recognition the performance than object trials, F(1, 23) = 10.3, p < 0.005. See Figure 2 for 24 hr associative recognition performance and Figure S1 available online for immediate associative recognition performance. Consistent with our predictions, based on the findings of Litman and Davachi (2008), LD object pairs were associated with better associative memory than SD object pairs, t(23) = 1.9, p < 0.05 on the 24 hr test. Crucially, LD object pairs were also associated with significantly reduced forgetting over the 2 test days compared to the SD object pairs, t(23) = 2.0, p < 0.05 (see Figure 2), consistent with the notion that reactivation after a longer intervening interval was associated with greater consolidation. Interestingly, we did not see the parallel effect for scene trials. Specifically, there was no significant difference between the LD and SD scene conditions in associative memory performance on the 24 hr test, t(23) = 0.

Suppression of PV cells with Arch stimulation caused an increase in Pyr www.selleckchem.com/products/Temsirolimus.html firing rate at all orientations. In relative terms, however, it increased responses less at the preferred orientation than at the orthogonal orientation. This resulted in a small but significant decrease of the OSI by −0.06 ± 0.08 (n = 31 Pyr cells; p < 0.001; Figure 3C; 13/31 individual cells showed significant changes in OSI). Activation of PV cells with ChR2 led to the opposite effect: a modest (but significant) increase in the OSI of Pyr cells (mean change in OSI: 0.07 ± 0.07; n = 14 cells; p < 0.003; 7/14 individual cells showed significant changes; Figure 3B). These small changes in overall selectivity depended systematically on the

change in Pyr SRT1720 clinical trial cell firing rate caused by PV cell perturbation. A linear regression of the percentage change in spiking response at the preferred orientation versus OSI revealed

a highly significant correlation (r = −0.6; n = 45 cells; p < 0.0001; Figure 3C). In other words, the Pyr cells that displayed the greatest increase in response also experienced the largest decrease in OSI. Conversely, the Pyr cells that displayed the greatest decrease in response experienced the largest increase in OSI. This said, the changes in OSI were minor even for the largest increases in Pyr cell firing rates: Pyr cells increased their response 3-fold before undergoing a change in OSI of only 0.1, a tenth of the distance separating an untuned cell from a perfectly tuned cell. As with orientation selectivity, the direction selectivity of Pyr cells changed only modestly while perturbing PV cell activity. Upon PV cell suppression the direction selectivity index (DSI, see Experimental Procedures) decreased by 0.08 ± 0.16 (over the population of n = 31 cells; p < 0.01; 7/31 individual cell had significant changes; Figure 3A). PAK6 Conversely, PV cell activation increased the DSI by 0.07 ± 0.11 (n = 14 cells; p < 0.05; 4/14 individual cell had significant

changes; Figure 3B). As with OSI, changes in DSI were small but highly significantly correlated with changes in response (r = −0.5; n = 45 cells; p < 0.001; Figure 3C). Remarkably, neither PV cell suppression nor activation had any systematic impact on tuning sharpness. We have already seen that PV cell modulation had no effect on the shape of the Pyr tuning curves for the two example neurons (see normalized tuning curves in Figures 3A and 3B). This effect was common to the whole sample. While perturbing PV cell activity slightly changed the tuning sharpness in a subset of Pyr cells (PV cell suppression: 9/31 cells; ΔHWHH = 7 ± 11 degrees; PV cell activation: 3/14 cells; ΔHWHH = −4 ± 9 degrees), there was no significant impact on the tuning sharpness across the population of Pyr cells (PV cell suppression: HWHH, mean change: 2.5 ± 14.6 degrees; n = 31 cells; p = 0.5; PV cell activation: −3.7 ± 8.2 degrees; n = 14 cells; p = 0.2).

However, a possible involvement of Ca2+-CaM-Munc13-1 signaling in release site clearance will have to be tested in future experiments. Munc13-1W464R KI calyces from P14–P17 mice exhibit

low PPRs at all inter-stimulus intervals tested (10–500 ms; Figure 7D), indicating higher release probability pr. Homeostatic processes leading to high pr were suggested to occur in the calyx of Held upon perturbation of synaptic transmission at the level of inner hair cells (Erazo-Fischer et al., 2007). It is thus possible that the high pr seen in Munc13-1W464R calyces may reflect a homeostatic compensatory mechanism that occurs in response to the physiological consequences of the Munc13-1W464R mutation in the calyx synapse or upstream of it. Alternatively, the high pr in Munc13-1W464R mutant calyces may indicate a modulatory LY294002 nmr effect of Munc13-1 activity on the release machinery. One such role was proposed based on the phenotype of neurons from KI mutant mice that carry a Munc13-1H567K mutation, which renders Munc13-1

insensitive to diacylglyerol and phorbol esters (Basu et al., 2007; Rhee et al., 2002). Cultured Munc13-1H567K neurons exhibit an increase in pr, which has been interpreted to reflect a gain-of-function effect of the H567K mutation, reducing the energy barrier for SV fusion buy AZD2281 downstream of SV priming (Basu et al., 2007). A similar scenario might arise

in the context of the Munc13-1W464R mutant calyces, which would be supported by our observation that at P14–P17, the fast time constant of release was slightly, albeit not significantly, faster in KI (τ1 = 0.8 ± 0.2 ms, 55%) compared to WT synapses (τ1 = 1.3 ± 0.5 ms, 60%; see Figures 4A and 4B), which is consistent with the slightly higher pr in the former. However, the H567K mutation likely destroys the zinc-finger structure of the C1 domain, thereby promoting an open conformation of Munc13-1 that mediates the gain-of-function effect. In contrast, the W464R mutation does not affect the α-helical structure of the Ca2+-CaM binding motif. Further studies are necessary to determine the reason Metalloexopeptidase for the increased pr in mature Munc13-1W464R calyces and how this might be linked to Munc13-1 regulation and synaptic function. In the present study, we used a combination of mouse genetics and electrophysiological recordings in the calyx of Held synapse to study the role of Ca2+-CaM-Munc13-1 signaling in presynaptic function and plasticity. With the Munc13-1W464R mutation, we were able to specifically pinpoint the role of Ca2+-CaM binding to Munc13-1 and to separate this process from the numerous other signaling pathways that are mediated by Ca2+-CaM and that may be affected upon pharmacological interference with Ca2+-CaM signaling.