Next, we will address how excitation spreads laterally between ON

Next, we will address how excitation spreads laterally between ON CBCs to provide insight into the mechanisms that initiate and propagate stage III waves. Voltage-clamp recordings showed that ON CBCs fall into two groups that receive excitatory input via distinct mechanisms. In one

group (II), stage III waves as well as focal glutamate applications on axon terminals appear to activate cation-nonselective conductances (Figures 6 and 7). In vision, ON CBCs hyperpolarize to glutamate release from cones via dendritically Bortezomib localized mGluRs coupled to Trpm1 channels (Koike et al., 2010, Masu et al., 1995 and Morgans et al., 2009). Based on our results, the most parsimonious conclusion is that during development group II ON CBCs express iGluRs on their axon terminals. While expression of these receptors needs to be confirmed and is likely transient, there is some evidence that even in mature circuits a subset of ON CBCs may utilize iGluRs (Kamphuis et al., 2003 and Pang et al., 2012). Wave-associated

spillover of glutamate into the extrasynaptic space (Blankenship et al., 2009 and Firl et al., 2013) combined with the expression of axonal iGluRs would provide a direct excitatory link between neighboring ON CBCs. In another group (I) of ON CBCs, gap junctions mediate depolarizations during waves and in response to focal glutamate applications (Figures 6 and 7). Which neurons form these gap junctions with ON CBCs? Candidates have to depolarize during the ON phase of stage III waves and be activated by glutamate. In mature circuits, ON CBCs are known to couple to AII ACs, which express iGluRs (Hartveit

and Veruki, 1997, Kolb and Famiglietti, 1974, Mills and Massey, 1995, Veruki and Hartveit, 2002 and Zhou and Dacheux, 2004). In addition to participating in visual processing, these electrical connections are involved in the generation of patterned spontaneous activity in retinas with Ketanserin photoreceptor degeneration (Borowska et al., 2011 and Trenholm et al., 2012). Among the diffuse ACs we recorded, four were morphologically identified as AII ACs. Each depolarized during the ON phase of stage III waves. Aside from excitatory coupling to ON CBCs, AII ACs likely participate in glycinergic crossover inhibition of OFF CBCs and OFF RGCs. In addition to AII ACs, ON CBCs may be coupled to other ACs (Farrow et al., 2013) and/or each other (Arai et al., 2010). Thus, gap junctions provide a second lateral excitatory link, either direct or via intermediate ACs, among ON CBCs. Both forms of excitatory input are recruited by glutamate released from ON CBCs, which we propose forms the basis for the generation and coordinated lateral propagation of stage III waves.

, 2006; Campbell and Gillin, 1987; Gierz et al , 1987; Walker, 20

, 2006; Campbell and Gillin, 1987; Gierz et al., 1987; Walker, 2010).

Neurons in the hippocampal cortex display distinct firing patterns during different behaviors (O’Keefe, 2007). Waking exploration and REM sleep are characterized by theta oscillations and neural firing “episodes” in which individual cells sustain elevated firing rates for several hundreds of milliseconds (Buzsáki, 2002; Louie and Wilson, 2001; Montgomery et al., 2008). HIF inhibitor In contrast, during immobility and non-REM sleep, hippocampal neural firing is concentrated in short (∼120 ms) sharp-wave ripple events, which synchronize activity across much of the network and have been suggested to reflect reactivation of learned firing patterns (Buzsáki, 1989; Wilson and McNaughton, 1994). Between ripples, neural firing is sparse and asynchronous for RG7204 supplier hundreds of milliseconds (Buzsáki et al., 1992; Csicsvari et al., 1999; Sullivan et al., 2011; cf. Carr et al., 2011). During sleep, hippocampal ripples are weakly correlated with neocortical slow oscillations (Steriade et al., 1993), although hippocampal activity is often dissociated from that of the neocortex (Hahn et al., 2007; Wolansky et al., 2006; Isomura et al., 2006). We examined the evolution of population firing patterns in the CA1 hippocampal region during sleep. Our findings show that discharge rates of both pyramidal cells and interneurons gradually ramp up during non-REM episodes,

interrupted by larger rate decreases during the interleaving REM epochs. This “sawtooth” pattern of rate changes across non-REM and REM episodes results in an overall downscaling of discharge rates over the course of sleep. In contrast, synchrony during non-REM ripple events increases from the early to late stages of sleep. The concurrent decrease of firing rates and increased population synchrony from one non-REM episode to the next are correlated with the power of theta oscillations during the intervening REM sleep. Our findings, therefore, suggest

a central role of REM sleep in regulating both discharge rates and synchrony in the hippocampus. Local field potentials (LFPs) and spiking activity of isolated CA1 putative pyramidal cells and putative interneurons were recorded Non-specific serine/threonine protein kinase in the home cage while the rat was immobile and assumed a characteristic sleep posture. The ratio of theta (5–11 Hz) and delta (1–4 Hz) power was used to identify non-REM and REM episodes (Figure 1A; see Supplemental Experimental Procedures available online), as described previously (Montgomery et al., 2008). Twenty-two sleep sessions (38.2 ± 5.8 min, SEM) with at least one non-REM-REM-non-REM cycle were recorded in five rats. Mean firing rates of pyramidal cells (n = 618) were similar between non-REM and REM episodes, whereas firing rates of interneurons (n = 111) were significantly higher during REM (p < 0.00018; sign-rank test; Csicsvari et al., 1999).

, 2004) Hypocretin-2 enhances glutamate release by presynaptic a

, 2004). Hypocretin-2 enhances glutamate release by presynaptic actions in the ventral tegmental area, a region of the brain involved in reward and motivation, and also potentiates NMDA receptor actions in the postsynaptic cell

through activation of protein kinase C (Borgland et al., 2008). Peptides can modulate a number of different channels or transporters that regulate neuronal activity and spike probability, including sodium channels, nonselective cation channels, sodium-calcium exchangers, and voltage-dependent calcium channels. Many inhibitory neuropeptides reduce GABA or glutamate release by activating G protein-coupled inwardly rectifying K+ (GIRK) channels, also called Kir3 channels. GIRK channels DAPT datasheet have become increasingly recognized as playing important roles in both normal brain processes, and in disease states (Lüscher and Slesinger, 2010). Different GIRK channels arise from the heteromeric assembly of different subunits (Luján et al., 2009); after Gi/Go activation, Gβ and Selleckchem JQ1 Gγ bind to the GIRK channel, resulting in hyperpolarization and inhibition. GPCR kinases can block GPCR function by phosphorylation-mediated internalization of the receptor; recent evidence suggests that the GPCR kinases can also directly

and rapidly inactivate GIRK channels by competitively binding Gβ and Gγ subunits, thereby reducing GIRK channel activity (Raveh et al., 2010). Neuropeptides that inhibit neuronal activity by activating

however GIRK channels include NPY, somatostatin, opioid neuropeptides including dynorphin and met-enkephalin and others (Nakatsuka et al., 2008; Nassirpour et al., 2010; Li and van den Pol, 2008). On the other hand, excitatory neuropeptides such as substance P (Koike-Tani et al., 2005) and hypocretin (Hoang et al., 2003) also act on GIRK channels, but inhibit the GIRK current to increase neuronal activity. Coupling of receptors to ion channels may be different in different processes of the same cell. For instance, mu opioid receptors that respond to met-enkephalin and other related opioid peptides often show fast desensitization of GIRK currents (Williams et al., 2001). Mu opioid receptor responses desensitize rapidly in the POMC cell body; in contrast, mu receptor responses are resistant to desensitization in the context of reducing GABA release from presynaptic axon terminals synapsing with the recorded cell (Pennock et al., 2012). Many neurons contain multiple neuropeptides (Hökfelt et al., 1986, 1990; Skofitsch et al., 1985; Zupanc, 1996).

Kohatsu et al (2011) showed that perfuming of females with cis-v

Kohatsu et al. (2011) showed that perfuming of females with cis-vaccenyl acetate (mimicking nonvirginity) reduces their ability both to physiologically stimulate P1 neurons and to provoke courtship behavior. This correlation

is suggestive that the P1 cluster integrates olfactory and gustatory sensory cues when weighing up the decision to court or not ( Figure 1). von Philipsborn et al. (2011) focused their attention on circuit elements downstream of P1 neurons. Through their original thermogenetic screen, they identified four additional classes of FruM neurons whose activation was sufficient to trigger wing extension or vibration. One of these, named pIP10, was male-specific and both necessary and sufficient to reproduce a faithful rendition of male pulse song, similar to the properties of P1 neurons. However, unlike

P1, pIP10 neurons innervate both higher brain centers (including the lateral protocerebrum) and the VNC, thus representing a putative descending (or “command”) neuron that transmits signals from the brain to initiate song (Figure 1). Other types of descending neurons are likely to exist, for example, those that select sine versus pulse song, or control song termination. One of these may be P2b neurons, which Kohatsu et al. (2011) identified in their screen as being sufficient, DAPT molecular weight although only partially necessary, to induce wing vibration. The other three FruM neuron classes characterized by von Philipsborn et al. (2011), dPR1, vPR6, and vMS11, were distinct from P1 and pIP10 in two significant ways: first, activation of these neurons did not lead to faithful recapitulation of pulse song. For example, vMS11 activation induced wing extension but no singing,

while vPR6 activation led to pulse song with a novel temporal structure. Second, all three types are contained within the VNC. These neural classes therefore represent candidate components or direct regulators of the song generator, and may correspond to some of the neurons previously shown to be sufficient to induce singing in decapitated males Bay 11-7085 and females (Clyne and Miesenböck, 2008). Indeed, while dPR1 is male-specific, vPR6 and vMS11 are present in both sexes, albeit exhibiting sexually dimorphic arborizations within the wing neuropil. Furthermore, the impact of vPR6 on pulse song patterning suggests these neurons are a “mutable” part of the song generator that might account for the diversity in courtship serenades critical for species recognition (Murthy, 2010). While physiological evidence for functional connections between P1, pIP10, and the thoracic FruM neurons awaits, von Philipsborn et al. (2011) assess overlap between axonal and dendritic arbors of these neural classes to predict potential synaptic contacts.

1) The percentages of orientation selective neurons (selectivity

1). The percentages of orientation selective neurons (selectivity index > 0.33, i.e., peak:null response > 2:1) were similar in areas V1 (58/78 = 74%), PM (30/43 = 70%), and AL (31/40 = 78%). Our estimates of orientation selectivity did not depend strongly on stimulus spatial frequency (data not shown) and are not likely to depend on temporal

frequency (Moore et al., 2005). We next considered direction selectivity across areas. Strong direction selectivity (index > 0.33, i.e., peak:null response > 2:1) was evident in 69% of V1 neurons (54/78), as compared to 42% of PM neurons (18/43) and 15% of AL neurons (6/40). V1 neurons were significantly more selective for direction than PM neurons (p < 0.02, K-S see more test, Figures 5B and 5C and Table 2). Neurons in AL showed less direction selectivity than neurons in V1 (K-S test, p < 10−7) and in PM (p < 0.01). These differences in direction selectivity between V1, PM, and AL cannot be explained by differences in peak response strength, which did not differ across areas (Table 2, K-S tests, all p values > 0.4; see Discussion).

However, the lower direction selectivity in AL compared to PM and V1 may be explained by our use of different stimulus temporal frequencies (8 Hz in AL, 2 Hz in PM and V1; see Moore et al., 2005), which were chosen to provide comparable response efficacy in each area (Table S1). We also investigated whether responses in any of these areas were biased to specific orientations or directions. The average normalized response across all neurons showed found a significant bias (to upward and downward drifting stimuli) in area AL selleck chemicals llc (ANOVA across eight directions, p < 0.001; see Figure S5A). Similar results were observed when considering

the preferred orientations and directions of individual neurons in area AL (Figures S5B and S5C). Population directional biases were not as clear in areas PM or V1 (all p values > 0.1). Together, these data indicate strong differences in response tuning between areas AL and PM, which suggests that these areas make distinct contributions to different visual behaviors (see Discussion). We tested whether these differences in response tuning between areas were present both during trials when the mouse was stationary and trials when the mouse was moving on the linear trackball. For this analysis, we selected all neurons in which we obtained robust estimates of spatial and temporal frequency preference both while the mouse was “still” and “moving” (same criteria as in Figure 3; V1: n = 35 neurons, AL: 27, PM: 8; Experimental Procedures). Temporal frequency tuning curves for two representative neurons, during still and moving conditions, are shown in Figure 6A. Consistent with a previous study (Niell and Stryker, 2010), locomotion led to a significant increase in peak response amplitude in V1 neurons (76%; paired t test, p < 10−4; Figures 6B and 6C).

This work extends our prior findings, which suggest that aggregat

This work extends our prior findings, which suggest that aggregate flux may occur in the setting of intracellular pathology, raising the possibility of therapies that can assist in aggregate clearance by targeting

extracellular mTOR activation species. This work has important implications for the design of therapeutic antibodies and suggests that targeting seeding activity in particular may produce the most effective agents. Several prior active and passive peripheral immunotherapy approaches against tau have also reduced tau pathology and improved behavioral deficits, but the underlying rationale for antibody choice was based either on a phospho-epitope, reactivity with neurofibrillary tangles, or was not stated (Asuni et al., 2007, Bi et al., 2011, Boimel et al., 2010, Boutajangout et al., 2010, Boutajangout et al., 2011, Chai et al., 2011 and Troquier et al., 2012). One tau immunization study, performed by vaccinating mice with full-length tau, induced pathology in wild-type mice (Rosenmann et al., 2006). However, subsequent active immunization approaches with phospho-tau peptides in tau transgenic models reduced tau pathology (Bi et al.,

2011 and Boimel et al., 2010) and showed behavioral improvement (Asuni et al., 2007, Boutajangout et al., 2010 and Troquier et al., INCB018424 2012). In a passive immunization study, JNPL3 tau transgenic mice were administered the PHF1 antibody intraperitoneally at 2–3 months of age, prior to the onset of tauopathy. PHF-1 targets a pathological form of abnormally phosphorylated tau (Otvos et al., 1994). Treatment reduced tau pathology and improved behavior (Boutajangout et al., 2011). However, while it decreased insoluble phosphorylated tau, next total insoluble tau

did not change. In another passive immunization study, JNPL3 and P301S mice (at age 2–3 months, prior to the onset of tauopathy) were peripherally administered the PHF1 or MC1 antibody, which targets an aggregate-associated epitope (Jicha et al., 1999). Both treatments improved tau pathology and delayed the onset of motor dysfunction (Chai et al., 2011). In these prior studies, the mechanism of action of the antibodies was not clear, and none was explicitly tested. Indeed, some proposed an intracellular mechanism (Sigurdsson, 2009). Moreover, no study appears to have produced the magnitude of reduction in tau pathology that we observed here, with the caveats that we infused antibodies into the CNS, while the other studies utilized peripheral infusion and different animal models were utilized. We designed this study explicitly to test a prediction that extracellular tau seeds are a key component of pathogenesis. We began with a selection process to pick antibodies capable of blocking tau seeding in vitro, purposely testing agents with a range of predicted activities.

Second, many amacrine cells—perhaps a majority of the total numbe

Second, many amacrine cells—perhaps a majority of the total number—perform find more some variety of vertical integration (the term is meant to contrast with lateral integration, as carried out by horizontal and wide-field amacrine cells). Only a small fraction of the 13 narrow field amacrine cell types found by MacNeil et al. (1999) were restricted to branching in narrow strata; the rest

communicate among several, sometimes all, of the layers of the IPL, like the cell shown in Figure 5. This means that they carry ON information into the OFF strata, and vice versa. This is termed crossover (for the crossing between ON and OFF layers) inhibition (because amacrine cells release GABA or glycine). It is the subject of very active investigation, which reveals a variety of interesting controls on the flow of information through the retina. The details are beyond the scope of this review, but an example is the finding

that some “excitatory” responses of ganglion cells to light are actually a release of amacrine mediated inhibition (Buldyrev et al., 2012; Demb and Singer, 2012; Farajian et al., 2011; Grimes et al., 2011; Molnar et al., 2009; buy JQ1 Nobles et al., 2012; Sivyer et al., 2010; Werblin, 2010). Third, most of the functions of amacrine cells are narrowly task-specific. An example is amacrine cell A17, a widely spreading neuron that places hundreds of electrotonically isolated synaptic boutons in contact with the output sites of the rod bipolar cell. At those points, the amacrine cell feeds back an inhibitory signal that improves the fidelity of information transmission by the rod bipolar

cell (Grimes et al., 2010; Sandell et al., 1989). This is the A17 cell’s primary, perhaps sole, task: and the A17 amacrine is in any case irrelevant to events that happen under daylight conditions. Another highly specialized amacrine cell, recently discovered in the ground squirrel retina, creates a specific receptive field property in a single type of ganglion cell (Chen and Li, 2012; Sher and DeVries, 2012). A blue-ON ganglion cell is well-known: it is excited by the blue-ON bipolar cell that selectively contacts blue cones. But electrophysiological recordings have encountered a blue-OFF ganglion cell, heptaminol inhibited when the stimulus lies at the short wavelength end of the spectrum. How can this happen if the only path through the retina is the blue-ON bipolar, carrying an excitatory signal? It turns out that a specific amacrine is driven directly by the blue-ON bipolar cell. The amacrine cell, like virtually all amacrine cells, is inhibitory to its postsynaptic partners. When excited by the blue-ON bipolar cell, this amacrine cell performs a sign inversion: it inhibits the ganglion cell upon which it synapses, thus creating a ganglion cell that is selective for blue stimuli and responds to a blue stimulus by slowing its firing—a blue-OFF ganglion cell. A final task-specific case is the role of the starburst amacrine cell.

To identify proteins that physically associate with PHF6, we used

To identify proteins that physically associate with PHF6, we used an approach of immunoprecipitation followed by mass spectrometry (IP/MS). We used a rigorous proteomics method that compares a specific IP/MS data set against a large set of unrelated parallel

IP/MS data sets, thus distinguishing high-confidence candidate interacting proteins (HCIPs) from background proteins (Behrends et al., 2010; Litterman et al., 2011; Sowa et al., 2009). Remarkably, AZD6738 concentration all four core components of the PAF1 transcription elongation complex, PAF1, LEO1, CDC73, and CTR9, were found as robust HCIPs of PHF6 (Figure 3A). We validated the interaction of HA-PHF6 and the endogenous PAF1 complex in coimmunoprecipitation analyses in cells (Figure 3B). Importantly, we also found that endogenous PHF6 associated with all four components of the endogenous PAF1 complex in mouse cerebral cortex at E17, coinciding temporally with migration of neurons to the superficial layers (Figure 3C). These data suggest that the PAF1 complex might represent a physiological interacting partner of PHF6. The PAF1 transcription elongation complex

was first identified in yeast as an RNA polymerase II-associated complex and plays a critical role this website in efficient transcriptional elongation along chromatin (Kim et al., 2010; Rondón et al., 2004; Shi et al., 1996). All four components of the complex were highly expressed during early development in primary cortical neurons and the cerebral cortex in vivo (Figure 3D). The role of the PAF1 complex in the brain has remained unexplored. We asked whether the PHF6-PAF1 interaction is functionally relevant in neuronal migration. We reasoned that if PHF6 acts via the PAF1 complex to regulate neuronal migration, loss of PAF1 would be predicted to disrupt neuronal migration. Consistent with this prediction, PAF1 knockdown by two distinct shRNAs substantially impaired neuronal migration in the cerebral cortex

in vivo, phenocopying the effect of PHF6 knockdown (Figures 3E, 3F, 3G, and 3H). Notably, knockdown of PAF1 led to downregulation of the other components of the PAF1 complex (Figure 3F) (Chen et al., 2009), suggesting that the intact PAF1 complex is required for proper neuronal migration. Collectively, these data suggest that PHF6 physically associates with the PAF1 Sodium butyrate complex and thereby drives neuronal migration. The finding that PHF6 interacts with the PAF1 transcription elongation complex and thereby promotes cortical neuronal migration led us to determine whether PHF6 exerts its function via regulation of gene expression. Because the PAF1 complex promotes transcription, we reasoned that PHF6 might stimulate the expression of genes that trigger neuronal migration. We performed microarray analyses of control and PHF6 knockdown primary cortical neurons. A large number of genes were downregulated in cortical neurons upon PHF6 knockdown (Table S1).

095) Further examination suggests that this

095). Further examination suggests that this selleck chemicals llc trend derives from differences among men and women in their chosen

running speed rather than an effect of speed per se. Running speeds (mean ± SD) for women and men were 2.98 ± 0.44 and 3.74 ± 0.59 m/s, respectively, and the difference was significant (p = 0.001, t test). As noted above, all but one woman used RFS while all but two men used MFS. Further, of the six adults with trials at both slow (<3.4 m/s) and fast (>3.4 m/s) speeds, none changed their foot strike usage at faster speeds. In fact, in all subjects with multiple recorded trials, none changed foot strike usage between trials. Thus, women were more likely to use RFS and to use a slower running speed than men. There is no evidence that subjects changed from RFS to MFS as speed increased. Results from bivariate comparisons were consistent with those of a multivariate nominal logistic regression. When speed, sex, and footwear (shod, barefoot) were used as independent variables predicting foot strike, only sex was a significant factor (p = 0.001). When adult and juvenile trials were pooled, both sex (p = 0.001) and age-class (p < 0.001) were significant predictors of foot strike usage, while speed (p = 0.157) and footwear (p = 0.101) were not. Foot, ankle, and knee angles at foot strike for

Hadza adults are Palbociclib concentration plotted against speed in Fig. 2. The effects of footwear, speed, and foot strike usage were entered into a multivariate nominal logistic regression to examine their effect on these angles. Not surprisingly, foot strike usage (RFS vs. MFS) was a significant predictor of foot angle at impact (p < 0.001), but speed (p = 0.54) and footwear (shod vs. unshod, p = 0.37) had no effect. Similarly, foot strike usage significantly predicted ankle angle at foot strike (p < 0.001), while neither speed (p = 0.21) nor footwear (p = 0.74) were significant factors. For knee angle, both foot strike (p = 0.006) and speed (p = 0.011) were significant factors, with more acute knee flexion at faster speeds, but footwear had no effect (p = 0.54). When juvenile trials are added to these comparisons, age-class does not significantly affect foot, ankle,

or knee angles (p > 0.05 all comparisons). Foot strike usage among Hadza adults was intermediate between that reported among the Kalenjin and Daasanach Megestrol Acetate populations (Table 1), and similar in some ways to the pattern reported for Tarahumara adults. When Hadza juveniles, adult men, and adult women are examined separately, some similarities with other populations emerge. Hadza men rarely use RFS (13.3% of subjects), similar to foot strike patterns of barefoot Kalenjin adolescents and Kalenjin adults who grew up barefoot, and to minimally-shod Tarahumara.6, 8 and 13 In contrast, Hadza women and juveniles often used RFS (90.9% and 85.7% of subjects, respectively), similar to Daasanach adults, habitually shod Kalenjin adolescents, and Tarahumara wearing conventional running shoes.

, Se

, FK228 order 2002). A targeted Rims1 mutation in the mouse leads to increased postsynaptic density and impaired associative learning as well as memory and cognition deficits ( Powell et al., 2004 and Schoch et al., 2002), and the frame shift allele

we found may lead to a similarly severe condition. Another intriguing candidate was the serine/threonine-specific protein kinase DYRK1A, which is located within the Down syndrome critical region of chromosome 21 and believed to underlie at least some of the pathogenesis of Down syndrome as a consequence of increased dosage. Several reports of likely inactivating mutations in DYRK1A result in symptoms including developmental delay, behavioral problems, impaired speech and mental retardation ( Møller et al., 2008 and van Bon et al., 2011), and a heterozygous knockout in the mouse also led to developmental this website delay and increased neuronal densities ( Fotaki et al., 2002). Truncating mutations in ZFYVE26 (encoding a zinc finger protein) are known to cause autosomal recessive spastic paraplegia-15,

consisting of lower limb spasticity, cognitive deterioration, axonal neuropathy and white matter abnormalities ( Hanein et al., 2008). It is possible that a heterozygous truncating mutation such as the de novo frame shift allele found in our study might cause a less severe version of this condition resulting in an ASD diagnosis. Other de novo mutations of interest were a 4 bp deletion in DST (encoding the basement membrane glycoprotein dystonin), which is associated with FMRP ( Darnell et al., 2011) and produces a neurodegeneration phenotype when inactivated in the mouse, and a nonsense mutation in ANK2 (an ankyrin protein involved in synaptic stability [ Koch et al., 2008]). A nonsense mutation in UNC80 has been linked next to control of “slow” neuronal excitability ( Lu et al., 2010). We also note that thirteen of the 59 LGD candidates appear to be involved in either transcription regulation or chromatin remodeling. Among the latter are three proteins involved in epigenetic

modification of histones: ASH1L, a histone H3/H4 methyltransferase that activates transcription (Gregory et al., 2007); KDM6B, a histone H3 demethylase implicated in multiple developmental processes (Swigut and Wysocka, 2007), and MLL5, a histone H3 methyltranserase involved in cell lineage determination (Fujiki et al., 2009). These three are also FMRP-associated genes. Fragile X syndrome (FXS) is one of the most common genetic causes of intellectual disability, with up to 90% of affected children exhibiting autistic symptoms. This has suggested overlaying recent understanding of FXS biology onto candidate ASD genes (Darnell et al., 2011). The FMR1 gene is expressed in neurons and controls the translation of many products.