A tiny piano thus becomes a hand-held object; a giant peach becomes a large object or landmark we can move

around. In Experiment 3, we examined whether these regions are tied to the object XAV-939 clinical trial category or whether the response reflects a more abstract concept of conceived size using a mental imagery task. Names of objects were presented aurally to a new set of observers, whose task was to form a mental image of each object. In half of the blocks, observers were told to imagine isolated objects at their typical size when they heard the object names (e.g., peach, piano). In the other half of the blocks, they were told to imagine an isolated object at an atypical size: specifically, they heard the adjective “tiny” for big objects and “giant” for small objects: e.g., “tiny piano,” imagined with the size of matchbox, or “giant peach,” imagined with the size of car (see Experimental

Procedures). Afterwards they were presented with small and big objects visually (as in selleck compound Experiment 1), to independently localize the big and small regions of interest in each subject. When participants imagined big and small objects at their typical sizes, the big and small regions showed more activity to objects with the preferred real-world size (Figure 5; Small-OTS-L: t(7) = 2.4, p = 0.048; Small-LO-L marginal: t(7) = 1.8, p = 0.107; Small-LO-R marginal: t(6) = 2.1, p = 0.083; Big-PHC-L: t(6) = 4.0, p = 0.007; Big-PHC-R: t(7) = 3.2, p = 0.015). These results are consistent with the fundamental and general finding that neural responses in object-selective cortex are similar between perception and imagery (O’Craven and Kanwisher, 2000, Reddy et al., 2010 and Stokes et al., 2009). Further, these results

also demonstrate that our previous results were not driven by pictoral artifacts of the stimuli: here, any perceptual features instantiated via imagery processes MTMR9 are meaningfully tied to object concepts and are not driven by unintentional feed-forward stimulus artifacts. When observers imagined big and small objects in the atypical-size conditions, the big and small regions did not reflect the conceived size of the object. That is, imagining a giant peach still activated the small-preference regions more than imagining a tiny piano (see Figure 4; Small-OTS-L: t(7) = 2.6, p = 0.036; Small-LO-L: t(7) = 2.4, p = 0.048; though not significantly in the right hemisphere Small-LO-R region: t(6) = 0.8, p = 0.45; Big-PHC-L and Big-PHC-R trending: both t(7) = 1.7, p = 0.13; see Table S2 for 2 × 2 ANOVA statistics). These results demonstrate that activity in these big and small regions does not reflect the conceived size of the imagined object—these regions are not reflecting an abstract sense of real-world size independent of the object identity.

This work was supported by the NSF (IOS 0542372, P.S.; DMR-0820492, D.K. [MRSEC program]), the HFSP (RGY0042- P.S.), the NIH (core grant P30 NS45713

to the Brandeis Biology Department; F31 DC011467, D.M.Z.; R00 GM87533, R.A.B.), the DGIST MIREBrain and Convergence Science Center (12-BD-0403) and Basic Science Research Program (2012009385) of the Ministry of Education, Science and Technology, Korea (K.K.), the Natural Sciences and Engineering Research Council of Canada (PGS-D3), and the Brandeis National Committee (S.J.N.), a gift from the Jensam Foundation (C.I.B.), and SAR405838 ic50 the Howard Hughes Medical Institute (C.I.B.). C.I.B. is an Investigator of the Howard Hughes Medical Institute. Author contributions: H.J., K.K., S.J.N., and D.M.Z. performed the experiments; E.M., D.K. and R.B. provided reagents; Capmatinib H.J., K.K., C.I.B., and P.S. analyzed and interpreted data; C.I.B. and P.S. wrote

the manuscript. “
“In most species, males and females display sex-specific behavioral repertoires. Courtship and mating behaviors elicited by pheromones are among the most obvious sexually dimorphic repertoires because they are innate and stereotyped (Stowers and Logan, 2010). What are the neural differences that give rise to different behaviors in each sex? Behavioral differences could be due to differences in the ability of each sex to detect pheromone or to differences in the processing of pheromone sensory information. For example, female mice with an impaired vomeronasal organ exhibit male mating behaviors, suggesting that the underlying neural circuitry is the same in both sexes but only active in males (Kimchi et al., 2007). It may be that females others are capable of smelling pheromones that males cannot and that smelling these compounds represses male mating. In this case, the difference is at the level of detection. Alternatively, male flies detect

pheromone identically to females (Kurtovic et al., 2007) but possess male-specific ganglia that initiate male courtship behavior (Clyne and Miesenböck, 2008; Kohatsu et al., 2011), even in an animal that is otherwise female (Kimura et al., 2008). Here, both sexes smell the same compound, cis-vaccenyl acetate, but male and female higher brain centers generate different responses ( Kurtovic et al., 2007). Thus, in this case, the difference is at the level of processing. The two mechanisms are not mutually exclusive. In Manduca sexta, transplanting the nascent male sensory apparatus (his antennae) to a female larva induces male development in the female brain, and the adult animal has male behaviors ( Schneiderman et al., 1986). The reciprocal switch generates an animal that has female behaviors ( Kalberer et al., 2010). In this case, a difference in detection induces sexually dimorphic wiring, resulting in a difference in processing. Behavior that depends only on differences in detection could be easily modulated, for example, by regulating chemoreceptor expression.

ALS affects 2 in 100,000 people and has traditionally been considered a disorder in which degeneration of upper and lower motor neurons gives rise to progressive spasticity, muscle wasting, and weakness. However, ALS is increasingly recognized to be a multisystem disorder with impairment

of frontotemporal functions such LDK378 research buy as cognition and behavior in up to 50% of patients (Giordana et al., 2011, Lomen-Hoerth et al., 2003 and Phukan et al., 2007). Similarly, as many as half of FTD patients develop clinical symptoms of motor neuron dysfunction (Lomen-Hoerth et al., 2002). The concept that FTD and ALS represent a clinicopathological spectrum of disease is strongly supported by the recent discovery of the transactive response DNA binding protein with Mr 43 kD (TDP-43) as the pathological protein in the vast majority of ALS cases and in the most common pathological subtype of FTD (Neumann et al., 2006) (now referred to as frontotemporal lobar degeneration with TDP-43 pathology, FTLD-TDP) (Mackenzie et al., 2009). A positive family history is observed in ∼10% of ALS patients (Gros-Louis et al., 2006), while up to 50% of FTD patients report family

members with FTD or related cognitive and behavioral changes (Graff-Radford and Woodruff, 2007), supporting the important contribution of genetic factors to these diseases. The most HIF inhibitor common currently known cause of familial FTLD-TDP involves loss-of-function Ergoloid mutations in the gene for the secreted growth factor progranulin (GRN) ( Baker et al., 2006 and Cruts et al., 2006). Although GRN deficiency has been directly linked to TDP-43 dysfunction and aggregation in a neuronal culture model of disease and in GRN knockout mice, the exact relationship between GRN insufficiency and TDP-43 dysfunction remains unknown ( Ahmed et al., 2010, Guo et al., 2010 and Yin et al., 2010). In familial ALS, ∼15%–20% of patients are found to have

mutations in the Cu/Zn superoxide dismutase gene (SOD1) ( Rosen et al., 1993). Treatments shown to be effective in SOD1 mouse models, however, have generally not been effective in ALS clinical trials, and the absence of TDP-43 pathology in cases with SOD1 mutations suggests that motor neuron degeneration in these cases may result from a different mechanism ( Mackenzie et al., 2007). For these reasons, the recent identification of mutations in TDP-43 (encoded by TARDBP) ( Kabashi et al., 2008 and Sreedharan et al., 2008) and the related RNA-binding protein fused in sarcoma (FUS) ( Kwiatkowski et al., 2009 and Vance et al., 2009) in ∼5% of familial ALS patients has significantly shifted the focus of ALS research and implicated abnormal RNA processing as a critical process in ALS pathogenesis ( Lagier-Tourenne et al., 2010).

This increase was independent of protein synthesis in response to E2 stimulation but was dependent on protein synthesis in response to L1 stimulation (Figure 2G). Similar data were also obtained when L1 was given GLU+SKF stimulation instead of GLU+FSK stimulation (Figures 2H and 2I). In addition, using our uEPSC-potentiation estimation method mentioned above, we found that this change in spine volume at E2 was accompanied by an increase in synaptic strength (Table 1; Figures S3F and S3G). Analogous to STC measured at a population level (Frey and Morris, 1998), STC at the single-spine http://www.selleckchem.com/products/Y-27632.html level is temporally bidirectional as GLU stimulation given to one spine (E1) prior to GLU+FSK stimulation given to a second spine (L2; Figure 2J) resulted

in the expression of L-LTP at both spines (Figure 2K). find more This expression of L-LTP required protein synthesis at L2 (Figure 2L). An important component of STC is that both the synaptic tag and the rate-limiting PrP(s) have limited lifetimes (Frey and Morris, 1997 and Frey and Morris, 1998). However, it has not been determined how different the two lifetimes are, a crucial point in understanding the dynamics of the temporal bidirectionality of STC. To determine the lifetime of the rate-limiting

PrP, we applied GLU+FSK stimulation to two spines (L1, L2) with anisomycin present only during L2 stimulation and varied the time between L1 and L2 stimulations. The efficiency of STC at L2, which would be proportional to the concentration of the rate-limiting PrP (Frey and Morris, 1997), was inversely related to the time between L1 and L2 stimulations (Figure 3A), with STC taking place only if L2 was stimulated within 90 min of L1 stimulation. These data suggest that the rate-limiting PrP decayed within 90 min. We obtained a similar time course of STC when we replaced GLU+FSK stimulation at L2 with GLU stimulation

without anisomycin (E2; Figure 3B). To determine the lifetime of the synaptic tag, we gave GLU stimulation to one spine (E1) before giving GLU+FSK stimulation to a second spine (L2), varying the time between E1 and L2 stimulations. We found that Electron transport chain STC efficiency, which is thought to be a measure of the tag strength (Frey and Morris, 1998), was also inversely related to the temporal interval between E1 and L2 stimulations, with STC occurring fully at an interval of 90 min but being abolished at an interval of 3 hr (Figure 3C). Thus, the temporal bidirectionality of STC is asymmetric as the lifetime of the tag (approximately 120 min, Figure 3C) is different from the lifetime of the rate-limiting PrP (approximately 90 min, Figures 3A and 3B). These data suggest that the temporal order in which information arrives at a dendrite is important in determining how it is consolidated as part of a stable engram. The ability to induce and observe STC at the single-spine level also allowed us to relate the magnitude of E-LTP expression at a single spine to the strength of the synaptic tag.

, 2002 and Noble, 2003). For DRD4, it has been suggested that Wnt activation carrying the 7R allele is particularly associated with one’s likelihood to experience craving for alcohol, rather than with more general alcohol phenotypes

(Hutchison et al., 2002). A second important issue involves the reference groups used, e.g. those adolescents that did not use alcohol or cannabis. By comparing regular users to abstainers, we tried to minimize the possibility that alcohol- or cannabis use related phenotypes were included in the comparison groups. However, because genetic effects on dopamine functioning have been associated with a broad range of reward-related disorders (Hyman et al., 2006), the absence of significant differences between regular users and abstainers

might be due to the inclusion of adolescents with selleck chemicals llc reward-related phenotypes in the comparison groups. However, Sakai and colleagues assessed the direct effect of DRD2 TaqIA on early onset alcohol use disorders in an adolescent sample with a high prevalence of comorbid cannabis use disorder and conduct disorder. Even when controls were selected for the absence of other substance use disorders and conduct disorder, no significant association between the A1 allele and early onset alcohol disorder was found (Sakai et al., 2007). We hypothesized that the effects of parenting would be moderated by the effects of the genetic risk markers in DRD2 and DRD4, in a way that adolescent carriers of these risk markers would be most vulnerable to the influence of less optimal parenting. We did not find support for this hypothesis. Except for an inverse and surprising association between L-DRD4 and parental not emotional warmth, indicating that higher levels of emotional warmth are associated with an increased risk of regular alcohol use in carriers of the L-DRD4,

parenting did not moderate the actual expression of a genetic predisposition in regular alcohol or cannabis use. While we do not know about previous studies reporting on these specific gene by parenting interactions with respect to cannabis use, findings by van der Zwaluw et al. indicate that low parental rule-setting towards alcohol consumption is associated with more alcohol use over time, particularly in adolescents that carry the A1 allele (van der Zwaluw et al., 2009). This inconsistency with our findings might be explained by the difference between the studies in alcohol-related phenotypes used (regular alcohol use versus frequency of alcohol consumption). Alternatively, we suggest that substance-specific rule-setting might be more strongly associated with subsequent adolescent substance use when compared to general parenting behaviors, and might therefore more easily trigger the actual expression of a genetic predisposition. Nonetheless, our findings did provide support for risk enhancing effects of parental rejection and overprotection, and a risk buffering effect of emotional warmth.

and Linda R. Dietel Philanthropic Fund at the Northern Piedmont Community Foundation, Tamkin Foundation, Jennifer Jones-Simon Foundation, Capital Venetoclax cell line Group Companies Charitable Foundation, Robson Family, and Northstar Fund. The project described was supported by grant numbers RR12169, RR13642, and RR00865 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH);

its contents are solely the responsibility of the authors and do not necessarily represent the official views of NCR or NIH. “
“(Neuron 48, 123–137; October 5, 2005) In the original paper, the middle initial was missing from Dr. Susan L. Patterson’s name in the author list. The author list is correct as shown here. “
“(Neuron 68, Smoothened inhibitor 948–963; December 8, 2010) The original publication omitted one affiliation for Dr. M. Petrovic: Institute of Medical Physiology, School of Medicine, University of Belgrade, 11000 Belgrade, Serbia. This affiliation has been added to the article online. “
“(Neuron 69, 423–435; February 10, 2011) The strains published by Menalled et al. (2003) and Lin et al. (2001) are known by alternative titles in the literature, either CAG140 or HdhQ140 for the Menalled et al. (2003) strain and either CAG150 or HdhQ150 for Lin et al. (2001). In preparing the manuscript, the strains were listed in the text and Table

1 as HdhQ140 and HdhQ150, respectively, but were referred to as CAG140 and CAG150, respectively, in Figure 1, a nomenclature error which was missed prior to publication. For the sake of consistency with the text, Figure 1 has been revised to name the strains first CYTH4 described by Menalled et al. (2003) and Lin et al. (2001) as HdhQ140 and HdhQ150, respectively. The corrected figure is included here and in the article available online. “
“Hanahan and Weinberg revisited and updated the hallmarks of cancer in 2011 based on the conceptual progress

of cancer in the last decade [1]. Two emerging hallmarks combined with previous six biological capabilities are widely accepted and acknowledged. They constitute the current eight hallmarks of cancer development and progression. These include: (1) stimulation of continuous proliferative signalling, (2) evasion of growth suppressors, (3) resistance to cell death, (4) potential of limitless replication, (5) induction of angiogenesis, (6) activation of invasion and metastasis, (7) deregulation of cellular energetics and (8) insensitivity of immune destruction [1] and [2]. A variety of stimulations and signals associated with tumour development are involved in one step or multiple steps of the eight hallmarks or other unapprehend processes to some extent. Tumorigenesis is a multistep and complicated process which is controlled by a cross-connected biological network. Tumour mass is not an independent entity only consisting of proliferative cancer cells.

Both observations are in agreement with a spread of SWR activity from proximal to distal sites in CA1 with respect to CA3. In addition, we found that ripple-associated cPSCs in pairs of pyramidal neurons were phase coherent, as demonstrated by coherence maxima in the ripple frequency range (Figure 3F). Cell-to-cell coherence maxima of cPSCs insignificantly decreased with increased spatial C59 purchase separation between cells (Figure 3G; R = −0.26, p = 0.26). In

line, comparison of cPSC coherence in close (<100 μm apart) versus distant (450–580 μm) neuron pairs revealed no significant difference ( Figure 3H; p = 0.39; rank-sum test). Together, these results on dual principal cell recordings confirm that ripple-locked cPSCs are indeed signatures of population oscillations. From the above experiments, MAPK Inhibitor Library it is not clear whether the observed synchrony is mediated by excitation, inhibition, or both (Figure S3B). To differentiate, we recorded from principal neurons at −66 mV, close to the reversal potential of Cl− (−67 mV in our conditions). By choosing this holding potential, we considerably reduced the driving force for Cl− and hence Cl−-driven GABAAR-mediated inhibition (see Figure S4A for the experimental confirmation of the Nernst potential). The kinetics derived

from spontaneous EPSCs (not associated with ripples) were fast enough to account for excitatory currents in ripple-associated cPSCs (Figure 4A). To corroborate this hypothesis, we quantified the temporal structure of ripple-coherent cPSCs. The underlying assumption was that rise times of synaptic currents are faster than their decays. At potentials below the reversal potential of excitatory synaptic transmission, excitatory currents within cPSCs are inward and should thus display downward slopes (rises) steeper than their upward slopes

(decays). In addition, at the potential we have chosen, putative inhibitory outward currents should display only small amplitudes, due to the small driving force for Cl−. We analyzed the slopes within Histone demethylase cPSCs in eight cells recorded at −66 mV (1,085 cPSCs in total). In line with EPSC kinetics, we found that downward slopes were indeed steeper than upward slopes ( Figure 4B): The analysis revealed slope values of 35.7 ± 0.5 pA/ms versus 18.9 ± 0.2 pA/ms for the populations of 10% strongest downward and upward slopes in individual cPSCs (p = 1.6·10−178; Kolmogorov-Smirnov test [K-S test]; n = 8 cells). We further checked whether the interval distribution of strong downward slopes can be related to ripples. Indeed, the incidence of strong downward slopes was in the range of ripple frequency as demonstrated by a peak at ∼5 ms in interdownward slope-interval histograms ( Figure 4C; see Figure S4B for single-cell analysis). Based on these findings, we hypothesized that the putatively excitatory PSCs are locked to the LFP.

We analyzed all postsynaptic partners (labeled and unlabeled, as shown in Figures 3E and 3F) of axonal boutons that contact mHRP-labeled dendritic processes, and found that presynaptic boutons contacting stable dendritic branches had fewer postsynaptic partners than those contacting extended branches (stable: 1.38 ± 0.06, extended: 2.19 ± 0.12 postsynaptic profiles/presynaptic bouton, n = 78 and 47, respectively, p < 0.001; Figure 3I). Furthermore, 79% of synapses on extending dendrites contacted MSBs whereas 38% of synapses on stable dendrites

contacted MSBs. Our previous studies showed that mechanisms that increased synaptic strength and maturation also stabilize dendritic branches (Haas et al., 2006), suggesting that synapses on stable branches may be more mature selleck compound than those on dynamic branches. Paclitaxel nmr We previously reported that the proportion of the presynaptic terminal area that is occupied by clustered synaptic vesicles increased during development when synapses mature and termed this metric the maturation index (Li and Cline, 2010). Here, we mapped the maturation index of synapses on stable, extended, and retracted branches (Figures 4A–4C). We found that synapses on stable dendrites had a higher maturation index compared to those on

extended dendrites (stable: 45.2 ± 1.7, n = 78; extended: 35.5 ± 2.5, n = 47, p < 0.001; Figure 4D). We also found that synapses Bay 11-7085 on retracted dendrites had a low maturation index (17.7 ± 10.2, n = 4 synapses), suggesting that disassembly of synaptic components occurs

prior to branch retraction, consistent with our previous in vivo imaging studies (Ruthazer et al., 2006) and studies in the neuromuscular junction (Colman et al., 1997). This analysis demonstrates that synapses on stable dendrites were significantly more mature than those on extended or retracted dendrites. Data presented above showed that synapses on extended branches tended to be clustered within 1 μm of each other. Analysis of synapse maturation relative to synapse distribution on extended branches showed that synapses that were clustered within 1 μm of each other were less mature, with an average maturation index of 28.9 ± 2.9 (n = 33), while synapses spaced further apart than 1 μm were more mature, with an average maturation index of 42.6 ± 5.0 (n = 12, p < 0.05; Figure 4E). By contrast, synapses on stable branches were relatively mature and their maturation indices were independent of the distance between synapses (maturation index of synapses within 1μm and larger than 1 μm: 45.6 ± 2.3 versus 44.7 ± 2.8, n = 47 and 28, respectively). This analysis indicates that extending branches tend to have clustered immature synapses, whereas synapses on stable branches are more mature and more sparsely spaced. The MSBs that contact mHRP-positive dendrites also contact unlabeled dendrites (Figures 3E and 3F).

, 2013). Oh and Gu (2013) found that the secreted Semaphorin 3E (Sema3E) is expressed at the developing whisker follicle. Sema3E is an interesting candidate for patterning the double-ring structure because it has been shown in independent studies to shape vascular and neuronal networks. In the developing Compound Library in vitro whisker of Sema3e mutant embryos or embryos lacking its receptor Plexin D1, the stereotypical “nerve inside – vessel outside” pattern

was severely disrupted. Both nerves and vessels targeted and remodeled around whisker follicles, but the two ring structures appeared intermingled. Further analyses revealed that this phenotype was the effect of the inward displacement of the vascular ring, whereas the nerve ring remained essentially unaffected. Thus, expression of Sema3E at the whisker follicle provides a repulsive signal for Plexin D1-expressing endothelial

cells that is required to maintain the vascular ring in its outer position. The observed lack of effect of Sema3E/Plexin D1 signaling on the sensory innervation of the developing whisker was surprising, given the expression of the Plexin D1 receptor in trigeminal ganglion cells and the repulsive effect exerted by the Sema3E ligand on these same cells in vitro. Here, the authors describe a mechanism leading to neutralization of Sema3E inhibition in vivo. Using see more a tagged Sema3E ligand as a probe to detect Plexin D1 expression, they showed that the receptor is heterogeneously distributed along the trigeminal axon pathway and is completely absent from the distalmost segments of the peripheral trigeminal branches. Not only may this local downregulation of Plexin D1 explain why nerve patterning occurs

normally in the absence of Sema3E/Plexin D1 signaling in vivo, but in a wild-type context it may also allow the nerve ring to maintain its inner position close to the source of the Sema3E repellent. If Sema3E does 3-mercaptopyruvate sulfurtransferase not directly affect nerve patterning, then how are trigeminal axons initially directed to innervate the whisker follicle? The NGF/TrkA signaling system is a probable candidate for this innervation, given that NGF is expressed around the whisker follicle and its TrkA receptor is present all along innervating trigeminal axons. Previous research reported that peripheral sensory axons fail to properly innervate the whisker pads in mutants lacking trkA ( Patel et al., 2000). In this study, Oh and Gu (2013) further show that sensory axons extend normally along the trigeminal nerve in the absence of NGF, but that they fail to innervate the whisker pads and to form a well-organized nerve-ring structure.

The most prominent feature in elp3 mutant HTS assay boutons is the occurrence of sizable T bars with large protrusions that extend into the cytoplasm ( Figures 4D–4G, arrows). Quantification of T bar top lengths (platforms) in controls indicates that they never exceed 300 nm, while in elp3 mutants we observe more than 20% of the T bars with a platform that is larger than 300 nm and up to 400 nm in length ( Figures 4D–4G, arrowheads; Figure 4H).

Thus, TEM indicates an increase in T bar size in elp3 mutants, and these data are consistent with the extensive “tentacles” extending into the cytoplasm in elp3 mutants that we observe in electron tomograms of elp3 mutant boutons ( Figures 4I–4O, arrows). In line with these data, we measure a concomitant increase in the number of synaptic vesicles that are in direct contact with the BKM120 datasheet T bar ( Figure 4P). Hence, the elaboration of the dense projections of the T bar in elp3 mutants results in an increased number of T bar-tethered vesicles. To determine functional consequences associated with the loss of elp3 at the NMJ, we measured synaptic transmission using two electrode voltage clamp. The average excitatory junctional current (EJC) amplitude

in 0.45 mM calcium is significantly increased in elp3 mutants ( Figures 5A and 5B), and also current clamp recordings indicate increased excitatory junctional potential amplitudes

in elp3 mutants Urease compared to controls ( Figure S4). To determine quantal content, we measured spontaneous vesicle fusion (mEJC) and quantified the quantal amplitude. As shown in Figures 5C–5F, mEJC amplitudes are significantly increased in elp3 mutants compared to controls, while the mEJC frequency trends toward an increase, but this is not statistically significant. The quantal content (in 0.45 mM calcium) also trends toward an increase but is not significantly different in controls and mutants (EJC/mEJC; controls, 45.3 ± 3.5 quanta; elp3Δ3/Δ4, 54.6 ± 6.7 quanta). Increased mEJC amplitude can be caused by larger synaptic vesicles that harbor more neurotransmitter or by a more elaborate postsynaptic glutamate receptor field. Given that synaptic vesicle size distribution in elp3 mutants is not different from controls, we labeled elp3 mutant NMJs with anti-GluRIIA8B4D2 antibodies and with anti-GluRIII/IIC antibodies that each recognize different glutamate receptor subunits ( DiAntonio et al., 1999 and Marrus et al., 2004). While we did not observe a difference in GluRIII/IIC labeling between elp3 mutants and controls ( Figures 5G, 5H, and 5M), the GluRIIA labeling in elp3 mutants is increased compared to controls, and this defect is rescued by a genomic fragment that harbors wild-type elp3 ( Figures 5I–5M).