ADAM10 is a type I membrane protein synthesized as an inactive proenzyme and has an N-terminal prodomain that is removed by furin or proprotein convertase 7 (PC7) in the trans-Golgi network in order for the protease to become active ( Figure 1B). selleck inhibitor Mature ADAM10 resides on the cell surface, where it performs ectodomain shedding of diverse membrane protein substrates, including APP. Although a major function of the ADAM10 prodomain is to maintain the enzyme in an inactive state during synthesis and maturation, the prodomain also functions as an intramolecular chaperone that assists in the correct folding of the enzyme’s various domains. The importance

of prodomain chaperone function is underscored by the observation that expression of a prodomain-deleted ADAM10 construct results in a proteolytically inactive enzyme, whereas coexpression in trans of the prodomain with prodomain-deleted ADAM10 rescues enzyme activity ( Anders et al., 2001). Given the role of ADAM10 as the major APP α-secretase in the brain, Rudy Tanzi and colleagues at Massachusetts General Hospital and Harvard University assessed the candidacy of ADAM10 as a LOAD susceptibility gene. In a previous

study, the group genotyped 30 SNPs that spanned ADAM10 and then performed targeted resequencing of the gene. This investigation identified two rare highly penetrant nonsynonymous mutations (Q170H and R181G) associated with LOAD in the prodomain of ADAM10 ( Kim et al., 2009). These mutations occurred in 11 of 16 GDC-0068 price affected individuals from seven LOAD-affected families. In cell-culture experiments, ADAM10 with either the Q170H or the R181G prodomain mutation exhibited α-secretase activity that was reduced by greater than 70%. In Idoxuridine addition, in cells coexpressing the prodomain mutants with APP, Aβ production was increased 1.5- to 3.5-fold. These results indicate that ADAM10 is indeed a LOAD susceptibility gene and suggest the intriguing possibility that the ADAM10 prodomain mutations reduce proteolytic

activity, even though they are located far from the active site of the enzyme. In their article in this issue of Neuron, the Tanzi group tested the role of the ADAM10 prodomain mutations in AD pathogenesis by generating transgenic mouse lines that express ADAM10 harboring the Q170H or R181G mutations in the brain ( Suh et al., 2013). They also made control mouse lines expressing an artificial dominant-negative (DN) mutation, E384A, or wild-type (WT) ADAM10. Multiple lines of each transgenic construct were created, and expression levels across the various transgenes were matched. In addition, the team crossed the different ADAM10 transgenic lines with the well-characterized APP transgenic mouse, Tg2576, to determine the effects of the ADAM10 prodomain mutations on Aβ generation and amyloid deposition in the brain.

Age-matched WT, ghsr−/−, or ghrelin−/− mice were housed individually for 1 week before food-intake measurements. Mice were kept in a standard 7 a.m. to 7 p.m. light cycle facility and fed with a regular mouse mTOR inhibitor chow. Mice were fasted for 16 hr before cabergoline and JMV2959 administration. Cabergoline (Tocris) was dissolved in 0.9% saline (1 ml), acidified with 2% of phosphoric acid (30 μl), and administered at 0.5 mg/kg doses. Either cabergoline in 100 μl of 0.9% saline buffer or 100 μl of 0.9% saline was administered intraperitoneally.

JMV2959 has been kindly provided by Aeterna Zentaris GmbH, Frankfurt, Germany.As described previously, JMV2959 was administered intraperitoneally ( Moulin et al., selleck chemicals llc 2007) at 0.2 mg/kg dose, 30 min before cabergoline treatments. Food intake was measured at 1, 2, 4, 6, 20, and 24 hr

after injection. The mean and the SEM are presented for values obtained from the number of separate experiments indicated, and comparisons were made using two-tailed Student’s t test or one-way ANOVA test. Data were analyzed using GraphPad Instat Software and differences judged to be statistically significant if p < 0.05. The authors gratefully thank Bryan Wharram for assistance with the food-intake experiments. The drd2−/− mouse brain was a gift from Emiliana Borelli (Department of Microbiology and Molecular Genetics, University of California Irvine). This work was supported by the grant from the US National Institutes of Health (R01AG019230 to R.G.S.). "
“A fundamental building block of neuronal circuits is the convergence of parallel streams of information onto single neurons. How a neuron combines these inputs into an output of its own shapes the computation that is performed by the circuit. Obtaining a functional description of how incoming signals are pooled

is therefore a crucial step for understanding neuronal information processing. unless Here, we study the rules of signal integration in retinal ganglion cells and ask how these cells combine stimulus components from different locations within their receptive field centers. In the retina, research on spatial integration of visual stimuli has focused on distinguishing linear and nonlinear integration by X-type and Y-type ganglion cells, respectively (Enroth-Cugell and Robson, 1966 and Hochstein and Shapley, 1976). Less is known, on the other hand, about what functional types of nonlinearities determine signal integration in the retina (Schwartz and Rieke, 2011). Parameterized model fits have suggested that Y-cell characteristics result from half-wave rectification in spatial subunits (Hochstein and Shapley, 1976, Victor and Shapley, 1979, Victor, 1988 and Baccus et al., 2008). Bipolar cell input into the ganglion cells has been identified as the likely source of this rectification (Demb et al., 2001), and rectified input currents have been directly measured in neurons of the inner retina (Molnar et al., 2009).

However, the target choice signals Matsumoto and Takada observed occurred after the monkeys fixated the target but before delivery of the reward,

implying that these, too, encoded an expectation of reward. In fact, the same signals were present in trials where the monkeys made incorrect choices, consistent with the interpretation that they reflected monkeys’ subjective expectations rather than the reward outcome or a prediction error. The authors’ most intriguing finding resulted from an analysis of which neural responses were present in which cells. Although nearly all cells responded to the onset of the reward cue, cells responding to the sample stimulus were found almost entirely in dorsal and lateral regions of the midbrain, probably within the SNc. By contrast, this website cells responsive

to the size of the search array were more concentrated in medial and ventral selleck chemicals llc regions, and there was a correlation between effect size and recording depth, most likely in the VTA. Such a gradient in function is broadly consistent with known anatomy: the SNc projects primarily to dorsolateral sensorimotor structures, whereas the VTA projects primarily to medial and limbic cortical areas associated with learning and motivation (Haber and Knutson, 2010). These observations endorse the authors’ conclusion that responses to the sample cue facilitate working memory by releasing dopamine in the dorsolateral prefrontal cortex. They are likewise consistent with the observation that factors influencing task difficulty are processed preferentially by systems responsible for calculating motivation and reward anticipation. Terminal deoxynucleotidyl transferase In addition to these tantalizing findings, the study also raises a number of important questions. Because the authors used spike waveforms to identify putative dopaminergic cells and recorded

only firing-rate responses, they could not verify the actual amount of dopamine released in response to task events; such verification could be provided by techniques such as voltammetry, which measures catecholamine release with millisecond precision. Furthermore, the difficulty of recording from small brainstem regions limited the number of cells recorded—enough so to suggest a gradient in function, perhaps, but the findings will benefit from replication. Finally, although both the location and timing of cell firing in response to the sample cue are consistent with the hypothesis that subsequent dopamine release facilitates working memory, future studies will need to verify this causally, perhaps by showing that selective activation or inactivation of lateral SNc neurons has an effect on the performance of working memory. What is most exciting about the work by Matsumoto and Takada is the finding that dopamine signaling in the brain is more heterogeneous and computationally specific than commonly thought.

, 2010) (Figure 3). Interestingly, while facilitation was prevented by philantotoxin (a blocker of Ca2+-permeable receptors), the inhibitory effect

involved PLC. Thus, the net result of activating presynaptic KARs by endogenous glutamate may depend on the glutamate concentration actually reaching the presynaptic KARs, which will ultimately depend on the magnitude of glutamate spillover arising from different patterns of synaptic activity coupled to the astrocyte uptake capacity. This hypothesis is further substantiated by data from the cerebellum, where bidirectional modulation of transmitter release has also been found. Synaptically activated buy SAR405838 presynaptic KARs facilitate and GSK2118436 datasheet depress transmission at parallel

fiber synapses (Delaney and Jahr, 2002). Activation of presynaptic KARs by synaptically released glutamate at parallel fibers facilitates glutamate release to both interneurons (e.g., stellate or basket cells) and Purkinje cells when these fibers are subjected to a regime of low-frequency stimulation. By contrast, with high-frequency stimulation, the synapses onto inhibitory interneurons are depressed, while synapses at Purkinje cells are still facilitated. Such differential sensitivities to the frequency of these two synapses may regulate the excitation/inhibition balance of Purkinje cells and, therefore, cerebellar output. Thus, at some structures,

KARs bestow computational properties to circuits according to the activity regime of afferent inputs. Presynaptic regulation of excitatory transmission by KARs has been studied extensively at MF-CA3 synapses. At these synapses, presynaptic KARs are implicated in the characteristic frequency-dependent facilitation of MF excitatory transmission (Schmitz et al., 2001, Lauri et al., 2001, Contractor et al., 2001 and Pinheiro et al., 2007), a phenomenon initially ascribed to the residual intraterminal calcium. Since KAR antagonists attenuate the potentiation of the second EPSC during high-frequency trains (e.g., 25 Hz; Schmitz et al., 2001), the synaptic activation of presynaptic KARs must be quite fast (10–30 ms), indicating that KARs should be found near the active zone. Indirect evidence suggests that the facilitation almost of glutamate release may occur through the depolarization of presynaptic terminals (Schmitz et al., 2001) that should enhance action potential-driven Ca2+ influx (Kamiya et al., 2002 and Lauri et al., 2003). The reduction of synaptic facilitation by a blocker of Ca2+-permeable KARs (Lauri et al., 2003) also points to a contribution of direct Ca2+ entry through these receptor channels, although Ca2+ mobilization from intracellular stores may also add to this use-dependent facilitation of glutamate release (Lauri et al., 2003 and Scott et al., 2008).

Importantly, when the frequency was increased to 200 Hz, just 3 to 5 stimuli were sufficient to achieve

charge transfer comparable or even stronger PS-341 concentration than in the control (AAV-EGFP) neurons, although the onset of the response was delayed by several milliseconds. Thus, while the temporal precision of transmission suffered, downstream neurons still responded to high-frequency spikes. Even long-term potentiation was retained in Syt1-infected animals. When the mice were tested in a contextual fear conditioning paradigm, the results with TetTox injections largely confirmed previous investigations using more traditional methods. Recent memory was impaired in animals with the virus injected in the hippocampus and entorhinal cortex, whereas remote memory (tested ISRIB mouse several weeks after fear conditioning and the virus injection) was affected only in the prefrontal group. However, the results with Syt1-infected mice were surprising. While recent fear memory was seriously impaired after entorhinal

Syt1 knockdown, Syt1 hippocampal mice performed just like the controls. Animals with Syt1 infections in the prefrontal cortex were comparable to their TetTox peers. In summary, high-pass frequency filtering of spikes by Syt-1 did not matter much in the hippocampus but was devastating in both the entorhinal cortex and prefrontal cortex. On the basis of these spectacular findings, Xu and colleagues (2012) suggest that different spike coding mechanisms are at work in the three different brain Bumetanide regions. Hippocampal circuits can rely on bursts of spikes only, whereas the paleo- and neocortex networks need high temporal precision of single

spikes for coding, at least for the mediation of contextual fear memory. The authors’ account of their findings may indeed be right. Yet, one might also consider the possibility that it is not necessarily the precision of spikes that matters, but rather the extent to which each structure is able to communicate via high frequency bursts, and thus overcome the genetic manipulation. As the authors point out, cortical neurons can fire both single spikes and complex spike bursts and the bursts may be critical for spike transmission under certain conditions (Lisman, 1997). Unfortunately, there is no natural frequency border between single spikes and spike bursts and the interspike interval statistic reflects a renewal process where spiking history is critical (Harris et al., 2001). Traditionally, a spike burst is defined as three or more spikes with < 8 ms intervals (Ranck, 1973). In the hippocampus, spike doublets and triplets of pyramidal cells at such short intervals occur 14% and 3% of all spikes during exploration. A burst of 4 spikes is rare (0.4%) and 5 or more spikes is super rare (0.06%) although these fractions can increase several-fold during sleep.

In principle, for receptors composed of two GluK2 and two GluK3 subunits, two arrangements are possible: (1) pairs of LBD homodimers,

one composed of GluK3 containing two zinc binding sites, with no zinc binding sites in the GluK2 homodimer; and (2) pairs of LBD heterodimers, each containing one zinc binding site formed by residues Q756, D759, CP868596 and H762 in GluK3 and D729 in GluK2 (Figure 8C). To distinguish between these two possibilities, we measured the effect of zinc on receptors composed of GluK2b and GluK3(D730A) in the presence of 1 μM UBP310 to record primarily the activity of heteromeric receptors. Application of zinc (100 μM) led to potentiation of currents (Figures 8D and 8E), similar to WT receptors, whereas for GluK3(D730A) mutant homomeric dimers, zinc potentiation was abolished (Figure 6E). This result is consistent with hypothesis (2). Moreover, in cells transfected with GluK2b(D729A) and GluK3, zinc did not potentiate currents (Figures 8D and 8E), strongly suggesting that the zinc binding site is lost in these heteromeric receptors. Again, this is consistent with hypothesis (2), namely that heteromeric GluK2/GluK3 contains at least an LBD heterodimer and, if composed of two GluK2 and two GluK3 subunits, is arranged as a pair of heterodimers PI3K inhibitor at the level of the LBDs. Our results identify zinc as a positive allosteric

modulator of KARs containing the GluK3 subunit and provide a molecular and mechanistic basis for this allosteric modulation. We identify critical amino acids at the interface between the LBD of two partner subunits that form a pocket

for zinc binding. Zinc stabilizes the interface by cross-bridging the two partner LBDs in the dimer. By its action as a counter ion that reduces repulsion between opposed aspartate side chains, hence strongly reducing desensitization, zinc binding translates into potentiation of the GluK3 response. Our data also provide a mechanistic and structural explanation for the specific properties of the GluK3 subunit of KARs and reveal important information about KAR architecture. In particular, our study provides a structural explanation for the functional differences between the two closely related KAR subunits GluK2 and GluK3 and about the probable arrangement of subunits in a heteromeric GluK2/GluK3 receptor, the only native GluK3-containing most receptor identified so far (Pinheiro et al., 2007). The positive allosteric modulation of KARs by zinc appears as a specific feature of GluK3. Homomeric GluK1 and GluK2, as well as GluK2/GluK4 and GluK2/GluK5, are inhibited by zinc in the concentration range that potentiates GluK3 (this study and Mott et al., 2008). The properties of GluK3, especially the fast desensitization and low agonist sensitivity, set it apart from the other KARs (Perrais et al., 2010; Schiffer et al., 1997). We previously showed that the properties of GluK3 are dominant over those of GluK2 when expressed in heteromeric combinations (Perrais et al.