8 ± 0.7-fold) and decreased significantly in the cytoplasm (by 60.4% ± 6.2%) in differentiating NPCs (Figure 3C). Therefore, these findings indicate that Axin accumulates in the nuclei of NPCs in response to differentiation signals. The nucleocytoplasmic shuttling of Axin is tightly controlled by the nuclear localization signal (NLS) and nuclear export signal (NES) of the protein (Cong and Varmus, 2004). To elucidate the specific roles of cytoplasmic and nuclear Axin, we generated two point mutants of Axin, allowing the protein to be expressed specifically in the cytoplasm (Axin-NLSm) or nucleus (Axin-NESm) (Figure 3D). Like wild-type Axin, the overexpression

of cytoplasmic Axin (Axin-NLSm) at E13.5 increased FK228 mw the proportion of GFP+ cells in the VZ/SVZ at E15.5 (Figures 3E and 3F), suggesting that cytoplasmic Axin enhances NPC expansion. Furthermore, the re-expression of Axin-NLSm in Axin-knockdown NPCs also led to NPC BKM120 pool expansion (Figures 3G–3L) specifically through the enlargement of the IP population (Figures 3H, 3J, and 3L). In contrast, the expression of nuclear Axin (Axin-NESm) (Figures 3E and 3F) or re-expression of the protein in Axin-knockdown NPCs depleted the GFP+ NPCs in the VZ/SVZ and promoted the differentiation of NPCs into neurons (Figures 3G–3L). Together with the nuclear accumulation of Axin in cultured NPCs upon differentiation (Figures 3A–3C), these findings strongly suggest

Rolziracetam that Axin in different subcellular compartments of NPCs specifically regulates the amplification and differentiation of NPCs; cytoplasmic Axin in RGs enhances IP amplification,

whereas Axin in the nucleus of IPs promotes neuronal differentiation of IPs. Next, we investigated the molecular mechanism that controls the trafficking of Axin between the cytoplasm and nucleus. Treating RGs with leptomycin B led to the nuclear accumulation of Axin (Cong and Varmus, 2004) (Figure S4A), suggesting that the nuclear enrichment of Axin is regulated by nuclear export. It was noted that the Cdk5-dependent phosphorylation site (Thr485) is located close to the NES of Axin (amino acids 413–423) (Fang et al., 2011) (Figure 4A). Although Axin phosphorylation at Thr485 (p-Axin) could be detected in wild-type mouse neocortices at E13.5, this specific phosphorylation was markedly reduced in cdk5−/− littermates (by 45.5% ± 4.3%; Figures 4B, S4B, and S4C), indicating that Cdk5 is a major kinase that phosphorylates Axin during neurogenesis in vivo. Importantly, the nuclear level of Axin was reduced in cdk5−/− neocortices (by 68.2% ± 5.1%) accompanied by an increased level of cytoplasmic Axin (2.0 ± 0.3-fold; Figure 4B). These results suggest that Cdk5-dependent Axin phosphorylation is critical for controlling the nuclear localization of Axin in the embryonic cerebral cortex. To explore the role of Cdk5-mediated Axin phosphorylation, we examined how Axin phosphorylation is regulated in NPCs.

, 2009). There may be a connection between PKC signaling and the Nedd4-1 regulation of glutamatergic activity, in that PKC-promoted endocytosis of glutamate transporter GLT-1 requires ubiquitin ligase Nedd4-2-dependent ubiquitination (García-Tardón et al., 2012). The differences between the outcome of studies described in the two paragraphs above reflect both timing of stress exposure in relation to testing, along with the qualitative nature of the stressors used, and they reveal the biphasic nature of stress responses by the PFC. Since the neurochemical responses, such as the release

of dopamine, during stress exposure are transient, the timing of cognitive testing is a critical factor, in which 4–24 hr may be a time of compensatory reactions. This will be essential to clarify in future studies both in terms of age dependency of both positive and negative effects of stressors as well as Bortezomib timing after stress exposure and the intensity and duration of the stressor. Repeated stress, such as 21 days of chronic restraint stress (CRS), causes functional and structural changes in the prefrontal cortex and amygdala, as well as the hippocampus VE-822 in vivo (McEwen and Gianaros, 2011), though these effects exhibit regional specificity (see Figure 2A). For example, CRS and chronic

immobilization caused dendritic shortening in medial prefrontal cortex (Cerqueira et al., 2007, Cook and Wellman, 2004, Liston et al., 2006 and Radley et al., 2004) but produced dendritic growth in neurons in basolateral amygdala (Vyas et al., 2002), as well as in orbitofrontal cortex (Liston et al., 2006). These actions of stress are reminiscent of recent work on experimenter

versus self-administered morphine and amphetamine, in which different, and sometimes opposite, effects were seen on dendritic spine density in orbitofrontal cortex, medial prefrontal cortex, and hippocampus CA1 (Crombag et al., 2005, Robinson et al., 2001 and Robinson et al., 2002). Indeed, there are clear indications that, besides substance abuse, many other aspects of brain function are subject to structural plasticity, including respiratory and motor control regions during exercise training (Nelson and Iwamoto, 2006 and Nelson et al., 2005), the nucleus accumbens after repeated sodium depletion causing increased salt appetite and enhanced amphetamine self-administration (Roitman et al., 2002), and the ALOX15 hippocampus during hibernation (Magariños et al., 2006 and Popov and Bocharova, 1992). Pyramidal neurons in layer 3 of all three regions of mPFC (AcG, PL, and IL) in male rats are affected by chronic stress, yet as noted below, there are important sex differences in some of these responses. Apical dendritic length shrinks by 20% in male rats, and this shrinkage is most pronounced in the distal apical dendritic branches, whereas the basal dendritic tree is unaffected (Bloss et al., 2010, Bloss et al., 2011, Cook and Wellman, 2004, Radley et al., 2004 and Radley et al., 2008).

In conclusion, by using high-field SE fMRI, we were able to show functional activation in an extensive network within the ventral temporal lobe and MTL and identified several additional face-selective areas, some of which may be homologous to human face areas.

Most activated areas were also activated under anesthesia, suggesting the network is to a large extent independent of conscious processes. Awake monkey fMRI experiments were performed on two healthy male monkeys (Macaca mulatta), weighing 14 (G03) and 16 kg (B04). We needed three experimental sessions for animal B04 and five sessions for animal G03. In each 17-AAG in vivo session, the monkey successfully performed the task for an average duration of 2 hr. All experiments were approved by the local authorities (Regierungspräsidium) and were in full compliance with the guidelines of the European Community (EUVD 86/609/EEC) for the care and use of laboratory animals. The primate setup and hardware for the awake monkey experiments were described in detail previously (see Goense et al., 2008 and Logothetis et al., 1999). Briefly, the monkeys were implanted with a custom-made MR-compatible headpost and extensively trained in a mock environment to acclimatize them to the scanner environment

and noise. FG-4592 During scanning the monkeys were seated in a custom-made primate chair with their head fixed to a predetermined location on the chair to ensure reproducible positioning in each session. The animal’s jaw and body motions were monitored by custom-designed sensors. Eye movements were monitored by using an infrared camera or implanted eye coil and data were analyzed with iView software (iView, Sensomotoric Instruments GmbH, Teltow, Germany). Anesthetized experiments were performed on three adult macaques weighing 7–12 kg (two males, C06 and L04, and one female, N08). The experimental setup and anesthesia protocol were similar to the procedures described in  Logothetis et al., 1999.

Anesthesia was maintained with remifentanil (0.5–2 μg/kg/min) and mivacurium chloride (3–6 mg/kg/hr). Physiological parameters were monitored and maintained within the normal physiological range as described previously ( Logothetis et al., 1999). In awake experiments visual stimuli were presented binocularly by using an SVGA fiber optic system Mephenoxalone (AVOTEC, Silent Vision) with a resolution of 800 × 600 pixels and frame rate of 60 Hz. The stimuli were 24 exemplars of faces, fruit, houses, and fractals (Figure 1A) and occupied 5° × 5° of visual angle. The smaller stimuli made it easier for the monkeys to maintain fixation. Images were black and white, normalized to the same mean intensity and contrast, and overlaid on a gray background with the same intensity as the mean intensity of the stimuli. All stimuli had similar power spectra. Fractals were used instead of scrambled images because the power spectra of fractals more closely match that of natural objects (Falconer, 2003).

19 Signals were then passed through a BNC adapter chassis that was interfaced with an analog-to-digital board within a personal computer. These signals were then converted to ground reaction force vectors and moments. Data were filtered using a second order recursive low-pass Butterworth digital filter with an estimated optimum cutoff frequency

of 12.53 Hz.19 A customized LabVIEW (National Instruments Corp., Austin, TX, USA) software program computed A/P and M/L TTS. A/P and M/L components of the ground reaction force data were analyzed separately for each subject, but the same procedure KU-55933 clinical trial was used for both components. First, the last 10 s of the ground reaction forces were analyzed to find the smallest absolute ground reaction force range for each component.19 These ranges were accepted as the optimal range of variation values.19 A/P and M/L components of the ground reaction force data were then rectified.19 An unbounded

third order polynomial was fit from the peak force to the last data point for each component.19 TTS for each component was the point where the unbounded third order polynomial was equal to or less than the respective optimal range of variation value.19 Average A/P and M/L TTS values for each treatment condition were computed in PASW version 18.0 (SPSS, Inc., Chicago, IL, USA). Alpha level was set a priori at p ≤ 0.05 to indicate statistical STAT inhibitor significance. One-tailed paired samples t tests compared SRS to control conditions for A/P and M/L TTS. Effect size d values were calculated for each t test. 22 Average percent improvements for each TTS measure were also computed for all subjects and average improvement of eight subjects who

improved with SRS (subjects who did not improve were removed). No improvements were defined as increased TTS with SRS over a control condition. Lastly, to provide insight on why some subjects did not improve with SRS, we computed effect size d values for comparing responders and non-responders on frequency of sprains, frequency of “giving-way”, and score on the AJFAT. SRS significantly improved A/P TTS over the control condition (SRS = 1.32 ± 0.31 s, Control = 1.74 ± 0.80 s; 3-mercaptopyruvate sulfurtransferase t(11) = −2.04, p = 0.03; d = 0.76). The average percent improvement for A/P TTS with SRS was 24% (n = 12) and increased to 34% (n = 8; SRS = 1.32 ± 0.35 s, Control = 2.01 ± 0.86 s) when four subjects who did not improve were removed. SRS did not affect M/L TTS (SRS = 1.95 ± 0.40 s, Control = 1.92 ± 0.48 s; t(11) = −0.20, p = 0.42; d = −0.07). The average percent improvement for M/L TTS with SRS was 2% (n = 12) and increased to 15% (n = 8; SRS = 1.75 ± 0.30 s, Control = 2.06 ± 0.50 s) when four subjects who did not improve were removed. Using effect size d values to detect mean differences, non-responders had greater mean values than responders on frequency of sprains, frequency of “giving-way”, and score on the AJFAT.

However, our data chart clearly the emergence of optimal decision making as observers are offered a chance to become familiar with the category statistics. This notion was also supported by fMRI analyses, which identified voxels

that responded to the interaction between volatility and decision Selleck ABT 199 entropy predicted by the Bayesian model in the ACC. One interpretation of these data is that the ACC contributes to choices that are informed by information about the rate of change of the environment, in line with previous lesion (Kennerley et al., 2006) and fMRI (Behrens et al., 2007) work implicating this region in making optimal use of past reward history to inform decisions. Analysis of brain activity at the time of the feedback also supported this contention. Using an ROI-based analysis, we found that the ACC region activated in concert with environmental volatility at the time of feedback in Behrens et al. (2007) was sensitive to “optimal updating” signals defined by the three-way interaction among angular update, estimated variability, and volatility. One

interpretation consistent with previous work is that at outcome time, the volatility of the environment is encoded in the ACC in a fashion that dictates the extent that subjects will learn from each outcome (Behrens et al., 2007); in the decision period, ACC activity is only modulated by the optimal level of uncertainty at times when subjects employ this optimal strategy (in this task, when the environment

is stable). We additionally Onalespib chemical structure found strong optimal updating signals at the time of feedback in the posterior cingulate gyrus, a brain region implicated in the representation of uncertainty about rewards (McCoy and Platt, 2005), and in the choice to make exploratory decisions (Pearson et al., 2009) in the nonhuman primate. Admittedly, our current data do not indicate the mechanism by which, or the cortical locus at which, participants switch between strategies. Indeed, one possible Astemizole candidate is the anterior insular cortex, where decision-related fMRI signals were predicted by all three strategies, and which has been previously implicated in controlling the switch between behavioral modes (Sridharan et al., 2008). However, this remains a topic for future investigation. Together, our findings suggest that participants adapt their decision strategy to the demands of the environment, moving toward statistically optimal behavior when the environment permits learning about stable and predictable categories (Nisbett et al., 1983). By contrast, in volatile environment, agents adopt a cognitive strategy that is fast and computationally frugal, and relies on maintenance processes subserved by the PFC.

, 2008a), confirming their neuronal identity. For studying the effects of expressing wild-type and chimeric receptors based on GluN2A and GluN2B, constructs were cotransfected with peGFP (ratio 1:1) to identify transfected cells. Coexpression at this ratio was confirmed in the case of pRFP (Papadia et al., 2008). After 48 hr, the transfected neurons were then either

subjected to electrophysiological analysis or their fate following an excitotoxic insult was studied. Pictures of GFP-expressing neurons were taken on a Leica AF6000 LX imaging system, with a DFC350 FX digital camera. Using the automated cell-finder function within the Leica AF6000 software, images of transfected neurons were taken both before and 24 hr after a 1 hr treatment with NMDA (20 μM). Cell death was

assessed by counting the number Sirolimus of surviving GFP-positive neurons. In the vast majority of cases, death was easily spotted as an absence of a healthy GFP-expressing cell where one once was. In place of the cell, there was in most cases (>90%) evidence of death in the form of fragmented neurites, fluorescent cell debris, and a pyknotic nucleus (Papadia et al., 2008). This confirmed that the cells were genuinely dying as opposed to more unlikely scenarios, such as quenching of eGFP fluorescence in a subpopulation of neurons. For each condition, 150–200 neurons were studied over several independent experiments. An identical experimental regime was employed for studying the influence of ICER expression

on vulnerability of GluN2B2A(CTR)/2A(CTR) EGFR inhibitors cancer and GluN2B+/+ neurons to NMDA-induced excitotoxicity. Neurons were transfected with vectors encoding eGFP and the inhibitory CREB family member ICER1 (Stehle et al., 1993), or a control vector (encoding β-globin). We have STK38 previously confirmed that ICER1 expression inhibits CRE-mediated gene expression in neurons (Papadia et al., 2005). The fate of transfected neurons following exposure to NMDA was then studied as described previously. To measure extrasynaptic NMDAR currents, synaptically located NMDARs were blocked by quantal activation-mediated blockade by MK-801, as previously described (Martel et al., 2009 and Papadia et al., 2008). Briefly, whole-cell NMDAR currents were recorded (100 μM NMDA, in Mg2+-free and TTX/PTX-containing recording solution), after which the agonist was washed-out the recording chamber for 2 min. Irreversible NMDAR open-channel blocker MK-801 (10 μM; Tocris Bioscience) was then applied for 10 min, effectively antagonizing NMDARs located at the synapse and experiencing the localized, quantal presynaptic glutamate release (Martel et al., 2009 and Nakayama et al., 2005). Following the 10 min incubation period, MK-801 was then washed out (2 min), and the resulting extrasynaptic NMDAR currents were acquired.

0266, unpaired t test; abl STM versus abl LTM, p = 0.0117, paired t test). This result suggests that only retrieval of the long-term

memory was impaired in the ablated fish, while short-term memory was spared. The effect of the surgery itself on fish vision or perception of pain was minimal because operated fish could efficiently learn the task. We found no effect on the basic free-swimming behavior after surgery (Figures S3A1, S3A2, S3B1, and S3B2). To examine whether the retrieval of the memory stored in the activated area is affected Selleckchem Dactolisib by the ablation of this site, we ablated the same area 5 hr after the training and tested for retrieval of the avoidance behavior 24 hr after the last training (Figure 3A2). In fish that underwent the ablation after training, the number of trials required for reaching the learning criterion significantly increased, comparing performance before and after ablation (Figure 3D, before [average in training session 3] = 9.6; after [average] = 23.6, p = 0.025, paired t test). In contrast, sham-operated fish showed no significant change Galunisertib datasheet after the procedure in the average trial numbers required for reaching the learning criterion (Figure 3D, before [average in training session 3] = 10; after [average] = 9.6, p = 0.35, paired t test). When the ablation

was performed 24 hr after the last conditioning session, we also observed the defect in the memory

retrieval (Figure S3C). Altogether, our results indicate that the identified telencephalic areas are required specifically for the retrieval and/or storage of a long-term consolidated behavioral program in zebrafish. To examine physiological changes in neurons within the activated area after learning, we performed loose patch-clamp recording of individual neurons residing within the activated area of learner fish and cue-alone fish 24 hr after the last training using a small-field imaging setup (Figure S2B). We determined the recording site either by direct observation of the calcium signals in HuC:IP fish prior to the recording or by locating the electrode on the averaged coordinates of activity centers for the active avoidance Sodium butyrate task as in the ablation experiment in wild-type fish ( Figure 4C). The activated area was contained in the parvalbumin (PV)-expressing area of the Dc and Dl regions, lateral to the sulcus ypsilonformis (sy), which marks the border between the Dm and Dc regions ( Figures S4A–S4D). Importantly, double staining of the activated area labeling by pontamine sky blue injection with PV immunohistochemistry revealed that the activated area under the large-field imaging setup indeed corresponded to that observed under the small-field imaging setup ( Figures S4B–S4D, see also full experimental procedures in the Supplemental Experimental Procedures).

, 2006; Dias-Santagata et al., 2007). To monitor cell cycle activation in the brains of tau transgenic flies, we immunostained for proliferating cell nuclear antigen (PCNA), an S phase marker abnormally re-expressed in human AD tissue (Busser et al., 1998) as well as tau transgenic flies (Khurana et al.,

2006). Overexpression of DRP1 or knockdown of MARF suppresses cell cycle activation (Figure 2D). Conversely, MARF overexpression and DRP1 knockdown increase cell cycle activation in brains of tau transgenic flies. Western blot analysis shows no alteration in levels of tau in genetically modified backgrounds, confirming that manipulation of DRP1 and UMI-77 nmr MARF do not alter toxicity of tau by simply increasing or decreasing the expression of the tau transgene (Figure S2A). We next explored the mechanism by which tau expression promotes mitochondrial elongation. We first evaluated expression of DRP1 and MARF by real-time PCR, but did not observe changes in mRNA levels ( Figure S3A). We then examined the subcellular localization of DRP1. Cytoplasmic DRP1 protein must translocate to the mitochondria to drive fission ( Frank et al., 2001). To visualize DRP1 in Drosophila, we used a transgenic strain carrying a 9.35 kb genomic rescue construct that has an

in-frame FLAG-FIAsH-HA tag after the start BMS354825 codon of DRP1 ( Verstreken et al., 2005). The presence of this genomic rescue construct does not have a statistically significant effect on tau expression, mitochondrial length, or neuronal toxicity ( Figures S2A, S3B, and S2C), consistent with modest expression of DRP1 from its endogenous promoter ( Figure S2D). Visualizing DRP1 by immunostaining for HA, we find that in control neurons DRP1 signal appears in discrete foci, almost exclusively localized to mitochondria ( Figure 3A, control, arrowheads).

Immunostaining for FLAG shows an equivalent DRP1 staining pattern ( Figure S3D). However, in neurons from tau transgenic animals DRP1 foci are infrequent, all and the majority of mitochondria do not colocalize with DRP1 ( Figure 3A, tau, arrowheads). Signal intensity profiles for DRP1 and mitoGFP verify the tau-mediated inhibition of colocalization ( Figure S3E). One possible explanation for the lack of DRP1 localization to mitochondria in tau transgenic neurons is that elongated mitochondria fail to recruit DRP1 normally. To test this idea we expressed MARF in the absence of tau. In neurons overexpressing MARF we observe the expected increase in mitochondrial length. However, in contrast to results from tau transgenic animals, elongated mitochondria in MARF transgenics do colocalize with DRP1 puncta ( Figure 3A, MARF, arrowheads). Thus, failure of DRP1 to localize to mitochondria in tau transgenic neurons is not likely to be a secondary effect of the mitochondrial elongation itself. Mitochondrial localization of DRP1 is also retained following MARF knockdown, as well as increased or decreased expression of DRP1 ( Figure S3F).

Soc. Neurosci. abstract 413.10) allows

subregions of thalamic nuclei to be targeted based on connectivity. Although there are still many unanswered questions about the role of the thalamus in perception and cognition, converging evidence from neuroimaging, physiological, anatomical, and computational studies suggests that the classical view of cognitive functions exclusively depending on the cortex needs to be thoroughly revised. Only with detailed knowledge of thalamic processing and thalamo-cortical interactions will it be possible to fully understand cognition. This work is supported by grants NEI RO1 EY017699, NIMH R01 MH064043, and NEI R21 EY021078. “
“Memories evolve over time, and many have come to consider that memories have two extended “lives” after the initial encoding of new information. The first, called consolidation, involves a Target Selective Inhibitor Library prolonged period after learning when new information becomes fixed at a cellular level and interleaved among already existing memories to enrich our body of personal and factual knowledge. The second, called reconsolidation, turns the tables on a memory and involves the converse process in which a newly consolidated memory is now subject to modification through subsequent reminders and interference. Here we propose that Venetoclax chemical structure the time has come to join the literatures on these two lives of memories, toward the goal of understanding memory as an ever-evolving organization of the record of experience. Since

the pioneering studies on retrograde amnesia, it has been accepted that memories undergo a process of consolidation (Ribot, 1882, Müller and Pilzecker, 1900 and Burnham, 1903). Immediately after learning, memories are labile, that is, subject to interference and trauma, but later they are stabilized, such that they are not disrupted by the same interfering events. It is well recognized that memory consolidation

involves a relatively brief cascade of molecular and cellular events that alter synaptic efficacy as well as a prolonged systems level interaction between the hippocampus and cerebral cortex (McGaugh, mafosfamide 2000 and Dudai, 2004). Here we will focus mainly on the latter. Linkage between the hippocampus and consolidation began with the earliest observations by Scoville and Milner (1957) on the patient H.M., who received a resection of the medial temporal lobe area including the hippocampus and neighboring parahippocampal region at age 27. H.M.’s amnesia was characterized as a severe and selective impairment in “recent memory” in the face of spared memory for knowledge obtained remotely prior to the surgery. Tests on H.M.’s memory for public and personal events have shown that his retrograde amnesia extends back at least eleven years (Corkin, 1984), and more recent studies of patients with damage limited to the hippocampal region also report temporally graded retrograde amnesia for factual knowledge and news events over a period extending up to ten years (Manns et al., 2003 and Bayley et al., 2006).

Whole-mount LacZ staining of control embryos crossed with Rosa26LacZ demonstrates recombination within the proximal peripheral nerve by E13.5 and most of the sciatic nerve by E14.5 (data not shown). E15.5 Erk1/2CKO(Dhh)

Rosa26LacZ embryos showed a morphologically similar pattern of recombination and peripheral nerve patterning as littermate controls ( Figures 5A and 5B). The distribution of Schwann cells in the mature, P20 Erk1/2CKO(Dhh) Z/EG phrenic nerve projections appeared similar to controls, with Schwann cells present up to the NMJ ( Figures S5B and S5C). These finding suggest Schwann cells take up relatively normal positions in Erk1/2CKO(Dhh) embryos. Although Schwann click here cells appeared to be present within the nerve, gross dissection of P18, Erk1/2CKO(Dhh) sciatic nerves revealed markedly decreased nerve caliber and increased translucency ( Figure 5C). Electron microscopy revealed a clear, striking reduction in the number of myelinated axons and an increase in the number of unmyelinated axons in Erk1/2CKO(Dhh) mice ( Figures 5D–5F). There was no change in the number of Schwann cell nuclei within the sciatic nerve and dying, pyknotic nuclei were not detected at this stage ( Figure 5G). These data show that loss of ERK1/2 in Schwann cell progenitors clearly inhibits myelination. The development of myelinating

Schwann cells involves the upregulation of numerous factors, including Egr2/Krox-20, S100β, and various myelin components (Jessen and Mirsky, 2005). Immunohistochemical analysis of P18 Erk1/2CKO(Dhh) sciatic nerves revealed a 77.8% ± 8.1% decrease in the number of Egr2/Krox-20 positive cells science and the expression of OSI-906 concentration S100β was nearly absent ( Figures 5H–5K). GFAP immunolabeling of non-myelinating Schwann cells appeared normal at this stage (data not shown). These findings suggest that ERK1/2 is required for the progression of the myelinating Schwann cell lineage after initial specification. In order to better understand the potentially diverse developmental mechanisms underlying ERK1/2 regulation of PNS development, we performed

microarray analysis on RNA extracts derived from E12.5 Erk1/2CKO(Wnt1) and wild-type DRGs. We did not detect overt changes in DRG neuron number at this developmental stage, suggesting the profile is a reflection of ERK1/2 regulated genes and not the loss of any particular cell type. 209 distinct genes met our inclusion criteria, which included 98 downregulated and 111 upregulated genes in Erk1/2CKO(Wnt1) samples ( Figures 6A and S6). Functional annotation of regulated genes revealed significant changes in mediators of transcriptional regulation, cell-cell signaling, intracellular signaling, and cell-cycle/division ( Figures 6A and S6). A number of genes involved in transcriptional regulation were modified that have been shown to regulate glial development (Figure 6B). Microarray changes were validated by qPCR of DRG samples from E12.