To understand the mechanism of Nak function, proteins coimmunoprecipitated

with Flag-Nak from embryonic lysates were analyzed to identify Nak-interacting partners. One major signal in the coimmunoprecipitates corresponds to β-adaptin (Bap), a subunit shared by AP1, AP2, and AP3. However, while AP2-specific subunits (α-adaptin and AP-50) were present in the precipitates, AP1- and AP3-specific subunits were absent, suggesting that Nak mainly interacts with AP2 in vivo (Figure 3A). The interaction between Nak and AP2 was confirmed by immunoprecipitation assay in S2 cells transfected with Flag-Nak (Figure 3B). The association of Nak with AP2 implies that Nak has a role in CME. Indeed, Nak is expressed highly in garland cells, which are active in endocytosis (Figure S1A, dashed arrow). Perifosine order We show that Nak is required for efficient

transferrin uptake by garland cells (Figures S3A–S3C). The endocytic role of Nak suggests that the dendritic defect in nak mutants is caused by disruption of CME. This predicts that dendrite arborization defects should be observed in neurons deficient in dynamin and α-adaptin, a GTPase required for vesicle scission and a subunit of AP2, respectively. We tested this by overexpression of the shibire (shi; the Drosophila dynamin) ts1 allele that dominantly blocks endocytosis ( Kitamoto, 2001). When larvae with shits1 expression in da neurons were raised at the nonpermissive temperature (30°C), dendrite growth Osimertinib was completely Ketanserin arrested ( Figure 3C). Also, the terminal axonal tracks of class IV da neurons in the ventral nerve cord were severely defected ( Figure S1F). As control, a normal dendritic pattern with fully elaborated terminal branches was observed in shits1 larvae raised at 18°C ( Figure 3D, dendrite number, 82.6 ± 4.2). To see if the severity of dendritic defects correlates with dynamin activity, shits1 larvae were raised at 22°C or 25°C to partially inactivate dynamin. Under these conditions, lower-order dendrites appeared normal, whereas the number and appearance

of higher-order dendrites were affected moderately at 22°C ( Figure 3E) and severely at 25°C ( Figure 3F). The number of dendrites was reduced to 77.8 ± 6.5 and 39 ± 2.9, respectively, indicating that the reduction in branch number inversely correlates with rising temperatures. Furthermore, the terminal branches were progressively shortened and often formed clusters at higher temperatures (open arrow in Figure 3F), resembling those observed in nak mutants. To inhibit α-adaptin activity in da neurons, UAS-α-Adaptin-RNAi was expressed with 109(2)80. Similar to nak mutants, shortened and reduced terminals were observed in α-Adaptin-RNAi da neurons (Figures 3G and 8B, column 3, endpoints, 59.3 ± 3.1). Thus, both dynamin and α-adaptin, components of the CME pathway, are required for higher-order dendrite arborization, consistent with the idea that Nak participates in CME to promote dendrite arborization.

We conclude that a small fraction of the heritability in schizophrenia can be found among intermediate sized de novo structural variants. The effect of de novo CNV on age at onset in bipolar disorder was nominally significant. These preliminary findings and similar results from previous studies (Priebe et al., 2011 and Zhang et al., 2009) suggest that individuals with an early onset of mania might constitute a subclass of bipolar disorder in which there is a greater contribution from rare alleles of large effect. Also consistent with this notion is a previous study (Grigoroiu-Serbanescu et al., 2001), which found that segregation of early-onset

BD in families was consistent with major gene effects, while familial segregation of late-onset BD was consistent with Selleck Venetoclax a multifactorial etiology. The observed rate of de novo CNVs in cases of bipolar disorder or schizophrenia with a positive family history of mental illness was similar to the rate Selleck Bortezomib in sporadic cases. These results contrast

with earlier findings by our group and others in autism (Marshall et al., 2008 and Sebat et al., 2007) and schizophrenia (Xu et al., 2008) documenting a higher rate of CNVs in sporadic cases as compared with subjects who have a positive family history. These early observations have not been universally replicated (Kirov et al., 2011 and Pinto et al., 2010). The reason for the inconsistency is not clear. Possibly, the initial findings were incorrect. Alternatively, variation in the observed effect could occur by chance or it could potentially be explained by methodological differences between cohorts in how mental illness in first-degree relatives Chlormezanone is ascertained. Pathway analysis

of genes impacted by de novo CNVs in SCZ lends support to independent findings from our group (Walsh et al., 2008) and others (Kirov et al., 2011) that rare CNVs in SCZ are enriched for genes that are related to synaptic function and other genes involved in neurodevelopment. By contrast, categories that were found to be enriched among de novo CNVs in BD were related to cell proliferation and shape and phospholipid metabolism (Table 5), the biological relevance of which is far from obvious. Greater knowledge of the specific genes involved in these disorders is needed to determine how these pathways might relate to the pathophysiology of disease. Our findings establish a contribution of rare CNVs and spontaneous mutation to risk for bipolar disorder. This can only be regarded as a starting point for studies of rare alleles in BD and SCZ. A larger fraction of the heritability must lie among different classes of alleles and will probably include rare and de novo point mutations and small insertions or deletions (indels).

All these amino acid exchanges occur on the solvent-exposed face of the inhibitor on its complex with thrombin ( Macedo-Ribeiro et al., 2008) and are therefore unlikely

to affect its learn more anticoagulant activity. Full-length boophilin and D1 were expressed in P. pastoris at high levels (21 and 37.5 mg/L, respectively) and purified by affinity chromatography on trypsin-Sepharose ( Fig. 2A and B). On SDS-PAGE, purified boophilin displayed an apparent molecular mass of 20 kDa and purified D1 of 11 kDa ( Fig. 2C). The inhibitory activity of boophilin against thrombin, trypsin and neutrophil elastase was assessed, and the corresponding inhibition constants (Ki) determined ( Table 1). Purified boophilin showed high selectivity to thrombin with a Ki of 57 pM, a value significantly lower than the 1.80 nM reported for boophilin produced in Escherichia coli ( Macedo-Ribeiro et al., 2008). The second Kunitz domain of boophilin displays an alanine residue at the reactive loop P1 position ( Schechter and Berger, 1967), suggesting it could inhibit elastase.

Both boophilin and D1 inhibited human neutrophil elastase in vitro with Ki values of 21 nM and 129 nM, respectively. Boophilin inhibits thrombin by binding simultaneously to the active site and the exosite 1 of the protease ( Macedo-Ribeiro et al., 2008). The contribution of the interaction with the exosite 1 to the inhibitory activity of boophilin was probed by comparing its activity towards α-thrombin and the exosite 1-less form, γ-thrombin next ( PLK inhibitor Fig. 3). Recombinant boophilin revealed no activity towards γ-thrombin, in amounts that completely abolished

the amydolytic activity of α-thrombin, therefore underscoring the importance of the interaction with the exosite 1. Different tissues of engorged R. microplus females were dissected and used for total RNA purification and cDNA synthesis ( Fig. 4). Boophilin gene expression was mostly detected in the midgut (25,000 fold above other tissues) with minor expression levels in hemocytes, although a contamination with midgut cells during dissection cannot be discarded. In an attempt to unveil boophilin’s physiological role, a RNAi-mediated gene silencing experiment was performed. Three groups of ticks, each composed of 25 animals, were injected with either boophilin dsRNA, PBS buffer or left untreated. In comparison to the control animals, an efficient silencing of boophilin expression was achieved after boophilin dsRNA treatment (Fig. 5A). Boophilin down-regulation resulted in a decrease (∼20% after 24 and 48 h) in egg production (Fig. 5B). Considering the important role of Kunitz-type inhibitors in the life cycle of R. microplus and the high specificity of the tandem Kunitz inhibitor boophilin for thrombin, full-length boophilin and its N-terminal Kunitz domain (D1) were expressed, purified and characterized.

e., Pdf-Gal4 > UAS-Mef2 flies, was associated with substantial arrhythmicity as previously reported ( Blanchard et al., 2010) ( Table 2). Flies with decreased Fas2 levels in LNvs also manifest constant defasciculation of s-LNv axons (albeit a weaker morphological phenotype than Mef2 overexpression; Figures 3B, 3C, and data not shown), and these flies had a substantially weaker behavioral phenotype than Pdf-Gal4 > UAS-Mef2 flies, namely, only

about 80% rhythmic flies on days 1–4 of DD and 69% rhythmic flies on days 5–8 compared to ∼98% for control strains (p < 0.01 Fisher’s test, Table 2). Similar morphological and behavioral phenotypes (p > 0.5 Fisher’s test, Table 2) were observed with Pdf-GAL4 > UAS-Fas2RNAi/UAS-Mef2RNAi flies. Importantly, overexpression of Fas2 in the Pdf-Gal4 > UAS-Mef2 background not only rescued the Selleck Autophagy Compound Library constant defasciculation of the background strain but also significantly increased the percentage of rhythmic flies (p < 0.01 Fisher’s test, Table 2). There was no significant change in Pfizer Licensed Compound Library rhythmicity due to the addition of an extra UAS element, i.e., PDF-GAL4 > UAS-Mef2/UAS-mCD8GFP is indistinguishable from Pdf-Gal4 > UAS-Mef2 (p > 0.5 Fisher’s test Table 2). These data strongly indicate that PDF neuron defasciculation contributes to the Mef2 overexpression phenotype. How is Mef2 itself regulated? CLK and CYC

ChIP-Chip experiments in our laboratory identified Mef2 as a direct target of CLK and CYC ( Abruzzi et al., 2011), and the Mef2 promoter manifests canonical cycling of CLK/CYC binding with peak levels at ZT14 ( Figure 5A). Indeed, previous expression analysis ( Kula-Eversole et al., 2010) demonstrated that Mef2 transcript levels cycle in l-LNvs with a peak phase consistent with this rhythmic CLK binding ( Figure 5B). As Mef2 transcript levels do not oscillate in whole Drosophila heads (see Figure 5B;

McDonald and Rosbash, 2001), we speculate that Mef2 is regulated by rhythmic CLK binding only in certain cell types (see Discussion). This notion is in agreement with the previously observed decrease of Mef2 staining levels within only PDF neurons in the clk and cyc mutants, Clkar and cyc01, respectively ( Blanchard et al., 2010). To verify that the link between CLK and neuronal plasticity goes through Mef2, we assayed the epistatic relationship between Clk and Mef2. As the loss-of-function Clk mutant ClkJrk leads to loss of s-LNv neurons ( Park et al., 2000; data not shown), we used RNAi to decrease Clk activity levels in PDF cells. The knockdown causes arrhythmic locomotor behavior (F. Guo and M.R., unpublished data) and disrupts rhythmic remodeling of s-LNv projections as expected. In addition to the loss of circadian plasticity, the Clk knockdown causes an overfasciculated phenotype, also characteristic of the Mef2 RNAi knockdown ( Figure 5C).

To obtain the statistical power to make quantitative comparisons between the effects of the two types of attention, the spatial attention data presented in Figure 3 include an additional 41 data sets for which we only obtained data from the orientation change detection task (50 data sets total).

Every aspect of the task was identical to the orientation change detection task used in the nine FRAX597 manufacturer data sets considered here, except that there were no interleaved blocks of the spatial frequency change detection task. These additional data sets have been described elsewhere (Cohen and Maunsell, 2009 and Cohen and Maunsell, 2010). To quantify attentional

modulation of the rates of individual neurons, we either took the difference between the mean responses to the stimulus preceding correct detections in the two attention conditions (Figure 3 and Figure 7) or computed an attention index by normalizing this difference by the sum of the mean responses in the two conditions (Figure 2). By convention, we expressed spatial attention modulation for each neuron as the mean response when attention was cued toward the stimulus in the contralateral hemifield minus the mean during the ipsilateral PARP phosphorylation hemifield condition. We chose to express feature attention as the mean response during the orientation change detection task minus the mean response during the spatial frequency change detection task. We defined pairs

of neurons with similar attentional modulation (Figure 3C and Figure 7) as those whose attentional modulation differed by <5 spikes/s (that corresponds to one spike in our 200 ms response window). We computed spike count correlations as the Pearson's correlation coefficient between spike count responses to the stimulus preceding the changed stimulus on correct trials within an attention condition. The sign of changes in correlation (Figure 3) followed the same conventions as changes in mean firing rate. We are grateful to Mark Histed, Adam Kohn, Amy Ni, already and Douglas Ruff for helpful discussions and comments on an earlier version of the manuscript. This work was supported by NIH grants K99EY020844-01 (M.R.C.) and R01EY005911 (J.H.R.M.) and the Howard Hughes Medical Institute. “
“When we search for an object in a crowded scene, such as a particular face in a crowd, we typically do not scan every object in the scene randomly but rather use the known features of the target object to guide our attention and gaze. In areas V4 and MT in extrastriate visual cortex, it is known that attention to visual features modulates visual responses (Bichot et al., 2005, Chelazzi et al.

Further information on the test/mask model, including alternate fits, is provided in Table S4. This work was supported by the Wellcome Trust through a Principal Research Fellowship to A.J.K. (WT076508AIA) and by Merton College, Oxford through a Domus A three-year studentship to N.C.R. We are grateful to Sandra Tolnai, Jennifer DAPT Bizley, and Kerry Walker for assistance with data collection. We also would like to thank Fernando Nodal, Douglas Hartley, Amal Isaiah, and Bashir Ahmed for

their helpful contributions to the surgical preparations. “
“Visual attention allows observers to focus on a subset of a complex visual scene. Spatial attention, which improves perception of stimuli at attended locations, has been well studied. However, observers can attend to many other attributes of a visual scene (Wolfe et al., 2004), including features (Haenny et al., 1988, Hayden and Gallant, 2009, Khayat et al., 2010, Martinez-Trujillo and Treue, 2004, McAdams and Maunsell, 2000, Motter, 1994 and Treue and Martinez Trujillo, 1999), objects (Blaser et al.,

2000, Houtkamp et al., 2003 and Serences et al., 2004), and periods (Coull and Nobre, Talazoparib 1998, Doherty et al., 2005 and Ghose and Maunsell, 2002). Whether all forms of attention employ common neural mechanisms has been debated extensively (Duncan, 1980 and Maunsell and Treue, 2006). Several psychophysical studies have argued that spatial attention is unique and that nonspatial forms of attention are inextricably tied to spatial location (Kwak and Egeth, 1992 and Nissen and Corkin, 1985). However, other studies argue that spatial and nonspatial forms of attention are qualitatively similar and might be mediated by equivalent mechanisms (Bundesen, 1990, Calpain Duncan, 1980, Keren, 1976, Rossi and Paradiso, 1995 and von Wright, 1970). Neurophysiological studies provide evidence supporting both views. Both spatial attention (Assad, 2003, Maunsell and Treue, 2006, Reynolds and Chelazzi, 2004 and Yantis and Serences, 2003) and feature attention

(Assad, 2003, Hayden and Gallant, 2009, Martinez-Trujillo and Treue, 2004, Maunsell and Treue, 2006, McAdams and Maunsell, 2000, Motter, 1994, Reynolds and Chelazzi, 2004, Treue and Martinez Trujillo, 1999 and Yantis and Serences, 2003) modulate the responses of individual sensory neurons: attending to a stimulus or feature that matches a neuron’s receptive field location or tuning preference typically increases neuronal responses. The similarity in the way different forms of attention affect individual neurons led to the hypothesis that all forms of attention use a similar neuronal mechanism (Martinez-Trujillo and Treue, 2004, Maunsell and Treue, 2006 and Treue and Martinez Trujillo, 1999). However, the retinotopic organization of visual cortex may allow spatial attention to employ a distinct mechanism because the comodulated neurons are typically located near each other.

B. would also like to thank Professor Terrence Sejnowski and members of the Computational Neurobiology this website Laboratory at the Salk Institute for Biological Studies for hospitality and a number of fruitful discussions. C.A. would like to thank Dr. Suhita Nadkarni for discussions and comments about the manuscript. “
“The brain is organized in a large number of functionally specialized but widely distributed cortical regions. Goal-directed behavior requires the flexible interaction of task-dependent subsets of these regions, but the neural mechanisms regulating these interactions remain poorly understood. Long-range oscillatory synchronization has been suggested to dynamically establish such task-dependent

networks of cortical regions (Engel et al., 2001, Fries, 2005, Salinas and Sejnowski, 2001 and Varela et al., 2001). Consequently, disturbances of such synchronized networks have been implicated in several selleck kinase inhibitor brain disorders, such as schizophrenia, autism, and Parkinson’s disease (Uhlhaas and Singer, 2006). However, in contrast to locally synchronized oscillatory activity, little is known about the global organization of long-range cortical synchronization. On the one hand, invasive recordings reveal task-specific synchronization between pairs of focal cortical sites (Buschman and Miller,

2007, Gregoriou et al., 2009, Maier et al., 2008, Pesaran et al., 2008, Roelfsema et al., 1997, Saalmann et al., 2007 and von why Stein et al., 2000), but require the preselection of recording sites and provide little information about the spatial extent and structure

of synchronization patterns across the entire brain. On the other hand, electroencephalography (EEG) and magnetoencephalography (MEG) measure synchronized signals across widely distant extracranial sensors (Gross et al., 2004, Hummel and Gerloff, 2005, Rodriguez et al., 1999 and Rose and Buchel, 2005), but it remains difficult to attribute these to neural synchronization at the cortical level. Hence, it has yet been difficult to demonstrate synchronization in functionally and anatomically specific large-scale cortical networks. The goal of this study was to test whether cortical synchronization is organized in such large-scale networks in the human brain. Furthermore, we aimed to characterize the spatial scale, structure, and spectral properties of such networks and sought to provide behavioral evidence for their functional relevance. We developed a new analysis approach based on cluster permutation statistics that allows for effectively imaging synchronized networks across the entire human brain. We applied this approach to EEG recordings in human subjects reporting their alternating percept of an ambiguous audiovisual stimulus. The ambiguous stimulus had two major advantages: First, perceptual disambiguation activates widely distributed cortical regions, including frontal, parietal, and sensory areas (Leopold and Logothetis, 1999, Lumer et al., 1998 and Sterzer et al.

Currents generated around AP threshold in CA3 neurons at −40 mV also increased following NO treatment or conditioning (Ctrl: −25 ± 80 pA, n = 9; NO: 377 ± 88 pA, n = 5; PC: 282 ± 145 pA, n = 5; p < 0.05), and this was suppressed by r-stromatoxin-1 (NO+Strtx: 82 ± 93 pA, n = 4) and by 7-NI treatment during conditioning (PC+7-NI: 107 ± 59 pA, n = 3), confirming a NO-dependent Kv2 current activation at potentials around AP threshold. Further evidence of the conductance

change was obtained by tail current measurements from the MNTB (Figures 3I and 3J) and CA3 (Figures 3K and 3L). Fit of a Boltzmann function showed that NO signaling (NO donor or PC) caused a marked leftward shift of the activation curve (V1/2) in neurons from both brain regions that was blocked by 7-NI or by glutamate receptor antagonism during the conditioning paradigm (Figures Selleckchem Smad inhibitor 3J and 3L). It is not possible to precisely equate half-activation voltages between recombinant and native K+ channels (because there are many unknowns in terms of heteromeric assembly, accessory proteins, and phosphorylation buy C59 wnt states), but such a leftward shift is consistent with a reduced contribution from Kv3 channels that have

a more positive half-activation voltage (Hernández-Pineda et al., 1999, Kanemasa et al., 1995 and Rudy and McBain, 2001) than Kv2 channels (Guan et al., 2007, Johnston et al., 2008 and Kramer et al., 1998). Additional evidence either for expression of Kv3 and Kv2.1 channels came from immunohistochemistry and qPCR experiments, showing Kv3.1b, Kv3.3, and Kv2.1 protein (Figures 4A, 4C, and 4D) and Kv3.1a, Kv3.1b, Kv3.2, and Kv3.3 mRNA (Figure 4B) in CA3 pyramidal cell bodies. We could not detect immunostaining (not shown) or substantial mRNA for Kv3.4. Together, these data confirmed that Kv3 channels are present in hippocampal CA3 pyramidal neurons as reported previously (Perney et al., 1992 and Weiser et al., 1994). We excluded significant contributions from Kv1, Kv4, and BK K+ channel families: Kv1 was routinely blocked with dendrotoxin-I (100 nM; data

not shown); Kv4 was inactivated by the conditioning voltage of the I/V protocol (Figure S2); and the NO-potentiated current was not a BK because this was TEA insensitive. We conclude that NO signaling mediates an activity-dependent adaptation in postsynaptic excitability by suppressing Kv3 and potentiating Kv2 currents in both the brain stem and hippocampus. These results suggest that neuronal delayed rectifiers are malleable; under low-activity conditions, Kv3 contributes to outward rectification, but during more active periods, Kv2 channels become dominant. This idea was tested in both MNTB and CA3 by examining the effect of TEA (1 mM) on AP waveforms under control conditions (before conditioning), on exposure to NO donors, or after synaptic conditioning (Figure 5).

, 2008) with minor modifications. Prior to bleaching, neurons were imaged every 30 s for 2 min at 15% laser power. For photobleaching, ROI was exposed to 75% laser power every 1.6 s for 40 frames. Recovery was monitored every 60 s

over 20 min at 15% laser power. To test for translation dependence, we pretreated cultures with 50 μM anisomycin for 30 min before the photobleaching www.selleckchem.com/products/AZD6244.html sequence. FRAP quantification and statistical tests are detailed in Supplemental Experimental Procedures. Dissociated DRG cultures were transfected with Dendra2 reporter constructs fused to Importin β1 3′ UTR axonal and cell body variants using Amaxa nucleofection. Dendra2 was photoconverted using a 405 nm laser at 5% energy power and 40× oil objective for 30 s. Images were captured every 4 min under the check details same conditions using 488 nm (0.1% energy) and 559 nm (4%

energy) lasers. The proximal region was photoconverted using a 405 laser at 0.5% energy power for 2 min every 4 min. Dendra2 quantification and statistical tests are detailed in Supplemental Experimental Procedures. L4/L5 DRGs were dissected from crush-lesioned or control animals at the indicated time points. Total RNA pooled from three animals was extracted using Trizol (TRI, Sigma-Aldrich). Total RNA (200 ng) was amplified, labeled, and hybridized on Illumina arrays (MouseRef-8 version 2.0 Expression BeadChip Kit). Data analysis was performed in the R environment using Bioconductor packages (http://www.bioconductor.org). Briefly, log2-transformed data was normalized using quantile normalization and differential expression analysis was performed using the LIMMA package as previously described Suplatast tosilate (Coppola, 2011). Total RNA was extracted from total DRGs pooled from three adult animals per replicate, using the Trizol reagent (TRI, Sigma-Aldrich) according to manufacturer’s instructions. Replicates consisting of at least 10 μg of total RNA each were processed for RNA expression analysis (RNA-Seq) on an Illumina Genome Analyzer IIx at the

High-Throughput Sequencing Unit in the Weizmann Institute of Science. RNA-Seq data was analyzed using DESeq (Anders and Huber, 2010). CatWalk training was carried out as previously described (Deumens et al., 2007). Motivation was achieved by a combination of food restriction during the initial training and placing of palatable reward at runway ends. Data were collected and analyzed with CatWalk software version 9.0 at days 0, 2, 4, 6, 8, 10, 14, 18, 22, and 26 postinjury. The analyzed indices are shown as a ratio between the ipsilateral (right) hind paw and contralateral (left) hind paw and are expressed as mean ± SEM. Quantification and statistical tests are detailed in Supplemental Experimental Procedures. We thank Erin Schuman for the myristylated GFP reporter, Freda Miller for the Tα1 tubulin promoter, and Fan Wang for the Advilin-Cre mice.

For each predictor, a series of fourth-order polynomials were used to model a potentially nonlinear response between the predictors and the BOLD signal. Importantly, the reported findings do not depend heavily on this particular analysis approach. A more conventional nonhierarchical voxel-wise linear model produced qualitatively similar results. ( Figures S2 and S3). All

reported stereotaxic coordinates refer to the MNI template and are reported as (x, y, z). Throughout, Selleckchem GSK3 inhibitor statistical maps have been thresholded voxel-wise at p < 0.01. An additional cluster extent threshold of 38 or more contiguous voxels enforced a whole-brain correction for multiple comparisons at p < 0.05 (see Supplemental Information). Thanks to Ross Mair, Tammy Moran, Caroline West, Miguel Cutiongco, David Dornblaser, Rebecca Hersher, and Allison Hyland for assistance, Marcus Johnson for assistance with

the eye tracker, Elissa Aminoff and Wilma Koutstaal for providing stimuli, and Scott Slotnick for providing software for the Monte Carlo simulation. This work was supported by NRSA AG034699 to S.A.G. and National Institute of Health MH060941 to D.L.S. “
“It is well appreciated that central nervous system (CNS) axons do not regenerate (Bradke et al., 2012). Peripheral nervous system (PNS) axons luckily do regenerate and mount a robust response because of an selleck products intrinsic regeneration program. This cell-intrinsic regeneration program (thought to be a reactivation

of the developmental program) is turned on by a retrograde injury signal that activates a transcriptional program (Figure 1) (Cavalli et al., 2005, Hoffman, 2010 and Liu et al., 2011). The difference between CNS and PNS neuron regeneration abilities is thought to be due to two factors: an “inhibitory” CNS environment and a “weak” activation of the intrinsic regeneration program. It is not known whether the weak activation of CNS neurons is due to differences in the intrinsic regeneration program or differences in the retrograde injury signal. The observation by Neumann and Woolf (1999) that a preconditioning cut to peripheral sensory axons suddenly allowed regeneration of their CNS axons was exciting to all who had long thought and the inhibitory environment of the CNS was an insurmountable barrier. The cell-intrinsic axon regeneration capability could overcome the CNS inhibitory environment! But why was a preconditioning cut required? Did the second cut induce a novel regeneration mechanism or just increase the normal intrinsic regeneration response above a threshold level needed for regeneration in the CNS environment? Much research has gone into identifying the molecular mechanisms responsible for the improved regeneration associated with a preconditioning injury (Hoffman, 2010).