In this sentence the positions of “D1” and “D2” are reversed, an error that has now been corrected in the article online. “
“An animal’s position in the local environment is monitored by a spectrum of functionally

specific cell types in the hippocampus and the adjacent parahippocampal areas, particularly the MEC. In the hippocampus, place cells fire selectively when the animal visits one or a few specific locations of the local environment (O’Keefe and Dostrovsky, 1971). In the MEC, grid cells fire at multiple locations that, for each cell, define a hexagonal grid that tessellates the entire space available to the animal (Hafting et al., 2005). Although the majority of cells in superficial MEC layers are grid cells (Sargolini et al., 2006), check details these cells intermingle with border cells, which fire whenever the animal comes close to one or several local geometric boundaries, such as the walls of the recording enclosure (Savelli et al., 2008 and Solstad et al., 2008). In layer III and deeper MEC layers, grid cells (Sargolini et al., 2006) also

mix with head direction cells, which fire only when the animal faces a given direction (Ranck, 1985 and Taube et al., 1990). The presence of multiple spatial cell types within the same brain system raises questions about their interrelationships. Place cells are probably generated from spatial inputs from the entorhinal cortex, the main cortical source of input to the hippocampus. The abundance of grid cells in the superficial layers Ku-0059436 order of MEC points to grid cells as a likely source for the place cell signal.

In several early models, place cell formation was explained by a Fourier mechanism in which periodic firing fields from grid cells with different grid spacing were linearly combined to generate single fields in hippocampal target neurons (O’Keefe and Burgess, 2005, Fuhs and Touretzky, 2006, McNaughton et al., 2006 and Solstad et al., 2006). This possibility has been challenged, however, by the observation that place cells mature faster than grid cells in young animals (Langston et al., 2010 and Wills et al., 2010). When rat pups L-NAME HCl leave the nest for the first time at postnatal day 16 or 17 (P16–P17), many place cells already have sharply confined firing fields similar to those of adult animals. In contrast, grid cells are far from fully developed. Firing fields are irregular and variable in size and shape and although some spatial periodicity can be observed in some neurons, adult-like patterns do not appear until approximately 1.5 weeks later, near the age of 4 weeks. The lack of sharply confined grid outputs in the 2.5- to 4-week-old nervous system has raised the possibility that juvenile place cells receive spatial information from other functional cell populations, such as the border cells of the MEC.

, 1986). BAG neurons have bag-like dendrites that extend near the lateral lips (Perkins et al., 1986 and White et al., 1986). Both URX and BAG neurons respond to changes in O2 in the environment but have different response properties and are associated with different behaviors. check details URX neurons depolarize in response to O2 increases, responding best to upshifts between 10%–12% to 15%–20% O2 (Zimmer et al., 2009). These neurons are essential for the aggregation behavior that C. elegans displays in response to high O2 and aerotaxis responses to O2 increases ( Coates and de Bono, 2002, Gray et al., 2004 and Zimmer et al., 2009). The BAG neurons, in contrast, respond to decreases in O2 levels, depolarizing

upon downshifts to preferred concentrations (5%) ( Zimmer et al., 2009). These neurons mediate aerotaxis response to O2 downshifts ( Zimmer et al., 2009). Soluble guanylate cyclases are expressed in the O2-sensing neurons and mediate recognition. C. elegans have seven atypical, β-like, soluble GCs ( Morton, 2004b), four of which have been shown to participate in hyperoxic avoidance. gcy-35 and gcy-36 are expressed in URX and together mediate responses to O2 increases ( Cheung et al., 2004, Cheung et al., 2005, Gray et al., 2004 and Chang et al., 2006). gcy-31 and gcy-33 are required in BAG neurons for responses to O2 decreases ( Zimmer et al., 2009)

( Figure 1). Guanylate cyclases are gas sensors that contain a heme-binding domain fused to a cyclase enzymatic domain that INCB018424 concentration converts GTP to cGMP ( Boon and Marletta, 2005).

For canonical GCs, the heme-binding domain selectively binds the reactive gas nitric oxide and excludes O2; a small change in the binding pocket of GCY-35 alters the ligand selectivity such that the heme binds O2 ( Gray et al., 2004). How do O2 increases activate URX while decreases activate BAG? For URX, the model is that GCY-35 and GCY-36 sense an increase in O2, activating the cyclase leading to cGMP production, the opening of cyclic nucleotide-gated (CNG) ion channels (TAX-2/TAX-4), and cell depolarization (Coates and de Bono, 2002, Cheung et al., 2004, Gray Resminostat et al., 2004 and Zimmer et al., 2009). For BAG, GCY-31 and GCY-33 are activated by a decrease in O2, triggering cyclase activity (Zimmer et al., 2009). Thus, the cyclases themselves are thought to show opposite responses to O2, with GCY-35/36 activated and GCY-31/33 inhibited by O2 increases. This model predicts that responses to increased and decreased O2 are the property of the cyclase not the neuron. Consistent with this, placing GCY-35 and GCY-36 in BAG neurons (in a gcy-31, gcy-33 double mutant background) causes these neurons to respond to O2 upshifts rather than downshifts ( Zimmer et al., 2009). Interestingly, Drosophila also contains three atypical guanylate cyclases that participate in O2-mediated behaviors: Gyc-89Da, Gyc-89Db, and Gyc-88E. Gyc88E clusters in a phylogenetic tree with C.

, 2003) in combination with optical fiber-based monitoring of population Ca2+ signaling activity (Adelsberger et al., 2005). The tip of the optical fiber (diameter 200 μm) was implanted above the stained cortical or thalamic area (Figure 1B). A column-like region with a diameter

of about 400–500 μm in mouse primary visual ALK inhibitor cortex was stained with OGB-1 (Figure 1C). In conditions of isoflurane anesthesia, slow oscillation-associated population Ca2+ transients occurred in the visual cortex at frequencies ranging from 8 to 30 events/min (Figure 1D, see Figure S4E available online), depending on the level of anesthesia (Kerr et al., 2005). It has been shown that Ca2+ transients are mediated by Ca2+ influx during the spiking activity Alectinib molecular weight in a local group of active cortical neurons (Kerr et al., 2005; Rochefort et al., 2009; Stosiek et al., 2003). In line with the previously used terminology (e.g., Rochefort et al., 2009), we refer to these population Ca2+ transients as Ca2+ waves. Figure 1I shows that spontaneous cortical Ca2+ waves are similar to those evoked by visual stimulation (Figure 1E) in terms of amplitude and duration. It is important to note that the comparison of Ca2+ wave amplitudes is meaningful only for a given site of optical recording, because the population of Ca2+ transients depends on

many local parameters, including the level of Ca2+ indicator inside cells and the intensity of the excitation light. Previous work has provided Ergoloid evidence that slow oscillations are initiated in the cortex (Sakata and Harris, 2009; Sanchez-Vives and McCormick, 2000; Timofeev and Steriade, 1996). To obtain deeper insights into the process of slow-wave initiation and propagation, we implemented an optogenetic approach. First, we used a transgenic Thy-1-ChR2 mouse line that expresses ChR2 in layer 5 neurons of the neocortex (Figure 1F)

(Arenkiel et al., 2007). When applying a single brief (50 ms) pulse of blue light through the optical fiber (Figure 1G) placed in the visual cortex, we obtained a reliable initiation of Ca2+ waves (Figure 1H). Light stimulation in C57/Bl6 mice failed to induce Ca2+ waves. Spontaneous, visually evoked, and optogenetically evoked Ca2+ waves recorded at a given cortical location had similar waveforms (Figure 1I) and virtually identical duration times and amplitudes (Figures S2A and S2B). The latencies of the onset of Ca2+ waves evoked by visual stimulation are quite similar to those evoked by brief (50 ms) optogenetic stimulation (Figure 1J). However, with shorter stimuli, optogenetically induced Ca2+ waves occur at longer latencies (Figure 1K). Not too surprisingly, Ca2+ waves can be evoked optogenetically not only in visual cortex (Figure S1A) but also in other cortical areas such as the frontal cortex (Figure S1B).

The correlation analysis between spiking in BLA cue-responsive neurons and LFPs from GC was measured for a 125 ms-wide bin either preceding (control) or following the onset of the tone. Eight, simultaneously recorded GC LFPs were used for each cell. The cross-correlation was computed on a trial-to-trial basis between the continuous LFPs and the rate histogram using a bin size of 1 ms.

The average cross-correlogram was computed for each cell-LFP pairing. To eliminate the influence from stimulus-induced covariation, a cross-correlogram was performed on pairs of signals coming from different trials (trial shuffle) and was subtracted from the average cross-correlogram on same trials. The peak occurring within buy SCH 900776 a ±50 ms lag of the resulting cross-correlogram was measured for both pre- and post-tone segments, and the values were compared with a t test. See Supplemental Experimental Procedures. See Supplemental Experimental Procedures. The authors

would like to thank Dr. Craig Evinger, Dr. Ahmad Jezzini, Dr. Giancarlo La Camera, learn more Dr. Lorna Role, Haixin Liu, and Martha Stone for the very helpful comments and discussions. A.F. would like to add a special acknowledgement to Drs. Don Katz and Arianna Maffei for their always insightful feedback. This work was supported by National Institute of Deafness and Other Communication Disorders Grants R01-DC010389 and R03-DC008885 and by a Klingenstein Foundation Fellowship (to A.F.). “
“We frequently hear that we are at the cusp of realizing the promise of molecular medicine. Nowhere is that promise closer to being realized than in the case of fragile X syndrome (FXS). FXS is now considered the gold standard for neurodevelopmental research because it has been barely 20 years from the identification of the gene responsible for the disorder to a putative molecular Liothyronine Sodium mechanism, resulting in multiple drugs undergoing clinical trials for treatment of patients. However, a concern of clinicians and hopeful parents has been “what if it’s too late”? Autism and related disorders such as FXS always have been thought to be irreversibly set

within a critical window during early childhood development between birth and 3 years of age. Often times, by the time FXS is diagnosed unambiguously, that window already has passed, which limits intervention options considerably. In the current issue of Neuron, Michalon et al. (2012) offer a bright ray of hope by comprehensively demonstrating the reversal of a wide variety of FXS phenotypes in adult mice with CTEP, a new selective antagonist of mGluR5 ( Figure 1). One of the most enduring hypotheses in FXS research has been the “mGluR theory,” which posits that many abnormalities associated with FXS are caused by excessive metabotropic glutamate receptor 5 (mGluR5) signaling. This excessive signaling results in exaggerated protein synthesis, which triggers an array of abnormal synaptic plasticity and behaviors (Bear et al., 2004).

, 2005 and Martinez-Trujillo and Treue, 2004). Neurons in area V4, for example, show enhanced responses to stimuli within selleck products their receptive fields (RFs) during visual search when they contain a color or shape feature that is shared with the searched-for target (Chelazzi et al., 2001), even when the animal is planning an eye movement (and, thus, directing spatial attention) to another stimulus

in the search array (Bichot et al., 2005). Thus, feature-selective attentional enhancement appears to occur in parallel across the visual field representations of extrastriate visual areas and presumably helps guide the eyes to searched-for targets. Although extrastriate neuronal responses are modulated by feature attention, to our knowledge, the source of the top-down feedback that biases responses in favor of the attended feature is unknown. During spatial attention, there is evidence that the response enhancement with attention observed in extrastriate visual areas results from top-down feedback from areas such as the frontal eye field (FEF) and lateral intraparietal area (LIP) (Desimone and Duncan, 1995, Gregoriou et al., 2009, Kastner and Ungerleider, 2000 and Serences and Boynton, 2007). Electrical stimulation of the FEF causes enhancement SAR405838 cell line of V4 responses and activation

of the cortex measured by fMRI, similar to what is found during spatial attention (Ekstrom et al., 2008 and Moore and Armstrong, 2003), and neurons first in the FEF and V4 synchronize their activity with each other in the gamma frequency range during spatial attention (Gregoriou et al., 2009). However, to our knowledge, whether these areas play the similar role during feature-based attention is still unknown. Like neurons in area V4, neurons in the FEF and LIP also show enhanced responses

to targets (or distracters that share features with the targets) compared to dissimilar distracters in their RFs, even when these stimuli are not selected for the next saccade during visual search (Bichot and Schall, 1999 and Ipata et al., 2009). This suggests that the responses of FEF and LIP neurons to stimuli in their RFs are influenced by the target features in parallel across the visual field, independently of spatial attention. However, the target stimuli used in these studies were fixed, at least within the same session, raising the possibility that the parallel effects of target features on responses arose from learning effects rather than flexible feature attention mechanisms. Learning effects on target responses have been found in prior studies in the FEF (Bichot et al., 1996). Indeed, one recent study of FEF neurons with a target that changed from trial to trial during visual search found that cells exhibited a serial shift of spatial attention effects from one stimulus to another in the search array, rather than parallel, feature attention effects (Buschman and Miller, 2009).

Many cloned ion channels have been shown to be regulated by GPCRs in this fashion (Hille, 2001). However, earlier patch-clamp recordings with the inclusion of GTPγS or GDPβS in the pipette to “lock” G proteins in active or inactive states, respectively, suggest that there are channel currents activated by GPCRs without the active involvement

of G proteins. One such current was recorded in cardiac myocytes, in which muscarine activated a Na+-dependent and TTX-insensitive MEK inhibitor inward current in the presence of GTPγS or GDPβS (Shirayama et al., 1993). Similar “atypical” G protein independent GPCR-activated currents have also been recorded from pancreatic β cells and from neurons in several brain areas (Heuss and Gerber, 2000 and Rolland et al., 2002). The ion channels and the mechanisms underlying this activity are largely unknown; to date, NALCN is one of the best-characterized channels activated in this GPCR-dependent, G protein-independent fashion. In several types of neurons, such as ventral tegmental area (VTA) dopaminergic neurons and hippocampal pyramidal neurons,

NALCN can be activated by neuropeptides such as substance P (SP) and neurotensin (NT) (Lu et al., 2009). The receptors for these peptides are GPCRs. However, the inclusion of GTPγS or GDPβS in the recording pipette does not prevent NALCN activation, suggesting G protein independence. The mechanisms underlying this G protein-independent INCB018424 NALCN activation are not fully understood but involve Src kinases, as the application

of Src family kinase inhibitors, such as PP1, abolishes NALCN activation by the neuropeptides. Likewise, activation of Src kinases by including a Src-activating compound in the recording pipette can bypass GPCR activation, resulting in NALCN-mediated currents (Lu et al., 2009). The activation of NALCN by SP also requires UNC80, which binds Electron transport chain Src and helps scaffold Src into the NALCN complex (Wang and Ren, 2009). A similar G protein-independent, Src-dependent activation of NALCN is also found in pancreatic β-cells upon stimulation with acetylcholine (Swayne et al., 2009). Since the activation of Src kinases lies downstream of many physiological stimuli such as neurotransmitters, growth factors, cytokines, cell adhesion molecules and mechanical stretch, these stimuli may regulate neuronal excitability via their action on NALCN. Both of the G protein-dependent and -independent regulation of NALCN via GPCR signaling converge onto UNC80 and NALCN but require different intracellular signaling molecules (G proteins versus Src family kinases) (Figure 4). In addition, these mechanisms rely on different structural components of NALCN.

In the control hemisphere, RSU firing showed remarkable stability over the entire 9 days of recording (n = 5 animals, Figure 2C, ANOVA, p = 0.98). In marked contrast, RSU firing in the deprived hemisphere was strongly modulated by MD. Data from a representative animal are shown in Figure 2B for baseline, day 2 of MD (MD2), and MD6; while firing was depressed on MD2, firing rebounded by MD6. The same pattern was seen in the entire population of sampled units (n = 7 animals, Figure 2D, ANOVA, p = 0.013). Interestingly, on MD1, there was no reduction in firing but by MD2 average firing dropped significantly, to ∼60% of baseline (Tukey-Kramer test, p <

0.05). This pattern is consistent with the observation that acute selleck products lid suture blurs and decorrelates visual drive but does not produce a large drop in average LGN firing rates (Linden et al., 2009) and suggests that between MD1 and MD2 decorrelated visual drive leads to an active suppression of V1m firing (Rittenhouse Small molecule library research buy et al., 1999; see Discussion). Crucially, over the next 2 days of MD (MD3–MD4), firing rates rebounded and by MD5–MD6 were indistinguishable from baseline. Although mean firing rates were ∼9% higher on MD6 (P32) relative

to baseline (P26), this increase was within the range of variation in the control hemisphere (Figure 2C) and was not significant (p = 0.98, Figure 2D, Tukey-Kramer test). If there was a dramatic change in the number of detectable neurons before or during monocular Rolziracetam deprivation, we might have under- or overestimated the size of the observed drop in firing and the subsequent rebound. However, the number of well-isolated units (indicated for each bar in Figure 2D) did not change significantly across days (chi-square test). Further, when we used conservative criteria to identify a subpopulation of individual RSUs we could follow for 2–6 days, this more stable population demonstrated the same biphasic pattern of firing during MD (Figures S2A–S2D). Finally, the same pattern of

drop and rebound was observed when we compiled average firing by animal (Figure S2E). Thus, the drop in averaging firing rate followed by a recovery to baseline is a robust feature of individual neurons under MD. To examine whether other aspects of neuronal firing were restored during prolonged MD, we compared the distribution of mean firing rates (Figure 2E), as well as the entire distribution of interspike intervals (ISIs) and ISI coefficient of variation (CV) (Figure 2F), as a function of days after MD. The entire distribution of mean RSU firing rates shifted to the left on MD2 (KS test, p < 0.01) and shifted back to become indistinguishable from baseline on MD6 (KS test, p = 0.33).

Next, we addressed the molecular role of Prdm8 within this repressor complex. Some members of the Prdm family have been shown to function as sequence specific transcription factors, while others are known to function as cofactors to mediate transcriptional repression (Davis et al., 2006, Duan et al., 2005, Gyory et al., 2004, Hayashi

et al., 2005 and Kim et al., 2003). Given the phenotypic similarity between Bhlhb5 and Prdm8 mutant mice, we first considered the hypothesis that a Bhlhb5 dimer is recruited to specific DNA elements through its consensus binding motif and then recruits Prdm8 to mediate transcriptional repression. If so, we reasoned that Bhlhb5 would bind normally to its DNA targets OSI-906 solubility dmso B-Raf inhibitor drug in the absence of Prdm8, but that Prdm8 would not associate with these sites in the absence of Bhlhb5. To address this hypothesis, we performed ChIP-qPCR from the dorsal telencephalon of wild-type or mutant mice and analyzed the binding of Bhlhb5 and Prdm8 at the RP58 promoter. As we had shown above ( Figures 5F and 5I), we again found that both Bhlhb5 and Prdm8 display robust binding to the proximal promoter of RP58 in wild-type mice ( Figure 6Bi). Furthermore, Bhlhb5 shows similar binding to

the RP58 promoter when ChIP-qPCR was performed in Prdm8 mutant mice, indicating that the binding of Bhlhb5 at this promoter is not dependent on Prdm8 ( Figure 6Bii). In sharp contrast, however, we found that Prdm8 was not bound to the RP58 promoter in Bhlhb5 mutant mice ( Figure 6Biii). Note that the observed absence of Prdm8 binding at this site is not due to a general absence of Prdm8 protein in Bhlhb5−/− mice (e.g., see Figure 1C). Thus, the inability of Prdm8 to bind to the RP58 promoter in the absence of Bhlhb5 suggests that Prdm8 requires Bhlhb5 for targeting to this genetic locus. Furthermore, the dependence of Prdm8 on Bhlhb5 for sequence-specific targeting to DNA appears to be a general phenomenon since we observed similar results when we tested several

other genomic loci including the Bhlhb5 promoter ( Figure S8A) and the no Bhlhb5 binding site in the first Cdh11 intron ( Figure S8B). Based on these findings, we suggest a model in which Bhlhb5 functions by binding to specific DNA elements possibly as a homodimer and then recruiting Prdm8 to mediate the repression of target genes (Figure 8A). When the Bhlhb5 alone is present, it can bind to target genes, but it cannot repress them. Likewise, when Prdm8 alone is present, target genes are also not repressed, in this case because Prdm8 does not bind to DNA in the absence of Bhlhb5. Thus, both factors are required to mediate transcriptional repression of a specific set of target genes so that, when either Bhlhb5 or Prdm8 is knocked out, common genes are upregulated and highly similar phenotypes result.

, 2011). However, subsequent work using recombinant synuclein has confirmed that even the nondenatured recombinant protein is intrinsically disordered and loses its α-helical conformation after dissociation from membranes (Fauvet et al., 2012). Loss of helicity find more could thus reflect the dilution inherent in preparing an

extract, with the helical state maintained at higher concentrations (Dettmer et al., 2013 and Wang et al., 2011), but NMR studies in E. coli have in fact suggested that macromolecular crowding maintains the disordered state of synuclein ( McNulty et al., 2006). It is also possible that synuclein folds to form a multimer only in mammalian cells, but the analysis of native brain synuclein has recently confirmed its almost entirely monomeric state ( Burré et al., 2013). Recently, it has also been shown that

synuclein can assemble into an oligomer (possibly tetramer) on nanoparticles ( Varkey et al., 2013), but this phenomenon seems to differ from the ability of a preformed tetramer to interact with membranes ( Wang et al., 2011). At this point, it remains possible that α-synuclein adopts a helical tetrameric state in solution, but the evidence is not definitive. The unavoidable dilution that accompanies purification of native synuclein complicates the analysis, but it is perhaps more important to acknowledge that despite extensive biochemical studies Nutlin3a in vitro, the conformation of synuclein in cells remains poorly

understood. In contrast to the N-terminal membrane binding domain, the C terminus of human α-synuclein is polar, with a higher proportion of charged residues. This domain undergoes unless phosphorylation at multiple sites (Oueslati et al., 2010 and Sato et al., 2013), suggesting a mechanism for regulation, but the function of the C terminus remains unclear, and it is the least conserved domain across species as well as among α-, β-, and γ- isoforms. The C terminus may affect membrane binding under particular conditions (Shvadchak et al., 2011), but phosphorylation toward the end of the N-terminal repeats, at Ser-87, more clearly affects membrane binding in vitro than phosphorylation at the other, more C-terminal sites (Paleologou et al., 2010). The observations thus suggest a potential biological role for Ser-87 phosphorylation, although this again remains to be identified in the context of the cell. The presynaptic location of α-synuclein has been recognized since its original identification as a protein associated with synaptic vesicles (Maroteaux et al., 1988). In contrast to many proteins involved in neurodegeneration that are distributed throughout the neuron, however, α-synuclein localizes specifically to the nerve terminal, with relatively little in the cell body, dendrites, or extrasynaptic sites along the axon (George et al., 1995 and Iwai et al., 1995).

To test this hypothesis in neurons, we analyzed the levels of F-actin in dendritic spines using phalloidin conjugated to Alexa 647. Spines on neurons transfected with a previously characterized small hairpin RNA (shRNA) against Arf1 (Volpicelli-Daley et al., 2005) exhibit significantly reduced phalloidin Sorafenib staining compared to controls, which is rescued by coexpression of shRNA-resistant WT-Arf1 but not by ΔCT-Arf1 (Figure 3A). This suggests

that Arf1 regulates F-actin levels via PICK1 in dendritic spines. F-actin undergoes a dynamic process of “treadmilling,” which involves the addition of actin monomers to the plus end of the filament and dissociation of monomers from the minus end. Recent studies have demonstrated that F-actin polymerization and depolymerization are highly regulated in dendritic spines (Hotulainen and Hoogenraad, 2010). To investigate this dynamic process, we used Lifeact-GFP, which binds F-actin in live cells, in conjunction with fluorescence recovery after photobleaching Antidiabetic Compound Library (FRAP) analysis. Expression of Lifeact-GFP in cultured hippocampal neurons results in a strong fluorescence signal in dendritic spine heads, consistent with the high levels of endogenous F-actin in spines (Figure S3B). FRAP of spine-localized Lifeact-GFP can be attributed to the

formation of new F-actin and hence is a measure of endogenous actin turnover. To confirm that FRAP of Lifeact-GFP in spines is not the result of simple diffusion of fluorescent Lifeact-GFP through the spine neck and/or exchange with bleached Lifeact-GFP on existing actin filaments, we stabilized actin filaments using jasplakinolide and carried out FRAP analysis on Lifeact-GFP-expressing spines. Figures 3B and 3C show that under control conditions, fluorescence levels recover quite rapidly with t1/2 =

14.9 ± 2.4 s. Jasplakinolide application dramatically slows the recovery, resulting in t1/2 = 250 ± 31 s. The minimal recovery that persists under conditions in which actin filaments are stabilized is likely to represent a small amount Adenylyl cyclase of exchange of bleached Lifeact-GFP and fluorescent Lifeact-GFP on existing actin filaments. This important control experiment demonstrates that the vast majority of the FRAP recovery can be attributed to dynamic actin turnover in the spine. To investigate the role of Arf1 in actin dynamics, we carried out Lifeact-GFP FRAP analysis on dendritic spines expressing Arf1 shRNA. Spines of similar size and morphology were selected for all conditions. Arf1 knockdown results in a significantly slower recovery compared to controls (Figures 3D, 3E, and S3C), suggesting a role for Arf1 in regulating actin turnover in dendritic spines. Coexpression of shRNA-resistant WT-Arf1 rescues the knockdown phenotype to control levels, whereas shRNA-resistant ΔCT-Arf1 does not rescue (Figures 3D, 3E, and S3C), suggesting that Arf1-PICK1 interactions regulate actin turnover in dendritic spines.