The molecular basis of dendrite-substrate interactions in vivo and the implications for dendrite morphogenesis remain incompletely understood. As dendrites elaborate, one important step in their patterning is the proper spacing of branches from the same cell, or sister dendrites, via repulsive dendrite-dendrite interactions (Grueber and Sagasti, 2010 and Jan and Jan, 2010). Self-avoidance, which ensures complete and nonredundant coverage of sensory or synaptic inputs, is most clearly observed in neurons that grow in a planar pattern, such as retinal ganglion cells, leech sensory neurons, and Drosophila dendritic arborization (da) neurons ( Grueber and

Sagasti, 2010, Jan and Jan, 2010 and Kramer and Stent, 1985). Although self-avoidance is probably not limited to two-dimensional arbors ( Zhu et al., 2006), the robustness of self-avoidance click here in such processes implies that molecules and substrates that restrict growth to a plane may influence repulsive interactions. The extent of this influence, and the impact on current molecular models of self-avoidance, is not known. Drosophila dendritic arborization (da) neurons have proven useful for studies of dendritic

morphogenesis and self-avoidance. da neurons can be Selleck C59 wnt segregated into four classes (classes I–IV) distinguished both by dendritic morphology and central axon projections ( Grueber et al., 2002 and Grueber et al., 2007). Numerous molecules have been implicated in control of dendrite-dendrite repulsion. For example, the Down syndrome cell adhesion molecule 1 (Dscam1) family of Astemizole homophilic adhesion molecules permits selective recognition between the surfaces of sister dendrites and initiation of repulsive responses between them ( Corty et al., 2009,

Hattori et al., 2008, Hughes et al., 2007, Matthews et al., 2007 and Soba et al., 2007). Dscam1 endows different neurons with unique surface identities via extensive alternative splicing to permit self versus nonself discrimination ( Corty et al., 2009, Jan and Jan, 2010 and Millard and Zipursky, 2008). Several genes have been found to promote repulsion between branches of class IV neurons, including tricornered (trc), which encodes a serine threonine kinase, furry (fry), and turtle (tutl), encoding an immunoglobulin superfamily member, however these appear to function independently of Dscam1 ( Emoto et al., 2004, Long et al., 2009 and Soba et al., 2007). Consequently, how Dscam1 and other factors combine to support self-avoidance is not currently known. One notable distinction is that Dscam1 is required for self-avoidance in all classes of neurons ( Hughes et al., 2007, Matthews et al., 2007 and Soba et al., 2007), whereas action of other molecules appears to be limited to the highly complex class IV neurons. It is not clear how self-repulsion mechanisms might differ between different classes of neurons, but understanding this distinction should begin to extend current models.

, 1979; Fletcher et al., 1999; Grottick et al., 2000), there are many types of serotonin receptor that have an excitatory net effect on dopamine (Alex and Pehek, 2007; Boureau and Dayan, 2011). In fact, an excitatory effect would actually be appropriate in some circumstances if the account about safety signaling is correct, as dopamine should respond

to the prospect of future safety engendered by the serotonergic report of possible aversion. Distinctions such as this may provide a route for helping understand part of the multiplicity of serotonin receptors (Cooper et al., 2002; Hoyer et al., 2002). As mentioned, whether the safety is achievable depends on the RG7204 cell line degree of controllability of the environment (Maier and Watkins, 2005; Huys and Dayan, 2009); how controllability is represented JQ1 mouse is not clear. In terms of the asymmetry, dopamine appears not to exert nearly such strong effects on 5-HT as vice-versa. Finally (K), a complex tapestry of heterogeneity is revealed, particularly within the serotonin system. We have also noted substructure in the dopamine system such as the mesocortical

dopamine neurons that are excited rather than inhibited by punishment (Brischoux et al., 2009; Lammel et al., 2011). Neuromodulatory representations of utility appear to play a central role in habitual control, not the least by controlling learning directly. Since goal-directed control is based more on predictions of specific outcomes, one might expect different neuromodulatory issues to arise. Indeed, there is direct evidence that dopamine plays little role in evaluation in the goal-directed system (Dickinson et al., 2000). Nevertheless, it can still influence the vigor of the execution of the responses which it mandates

(Palmiter, 2008). We noted that goal-directed Endonuclease (Dickinson and Balleine, 2002; Balleine, 2005) or model-based (Daw et al., 2005; Doya, 2002) control exhibits fuller flexibility in the face of factors such as changes in motivational state. This requires that the utility of predicted outcomes can be assessed under the current motivational state. In turn, this suggests a role for direct and/or indirect neuromodulatory influences over neural structures such as gustatory insular cortex or possibly the basolateral nucleus of the amygdala involved in such evaluation (Balleine, 2005, 2011) as providing information about that state. However, although we may be able to predict the values of some outcomes under expected future motivational states, there appear to be definite limits to such predictions (Loewenstein and O’Donoghue, 2004), perhaps because of constraints on the subjunctive determination of neuromodulatory state. This would limit any such prospective somatic marker (Damasio, 1994).

Neurons were transfected with GFP, a marker for cytoplasmic volume, and stained for endogenous dynactin and dynein. In the distal neurite, we observed a striking enrichment of dynactin but not of dynein, as compared to soluble GFP (Figure 2A). We saw a similar distal enrichment of dynactin in primary cortical, motor, and dopaminergic neurons, suggesting that this is a generally conserved mechanism (Figure S2). Line-scan analysis of the DRG neurons showed that dynactin accumulates in the

distal neurite significantly more than dynein (Figure 2B). These data suggest that dynactin is specifically recruited and/or retained in the distal neurite. Next, we asked whether the CAP-Gly domain is necessary for this distal enrichment of dynactin. We overexpressed wild-type or ΔCAP-Gly p150Glued in primary DRG neurons using a bicistronic vector that also expresses GFP. Wild-type p150Glued selleck chemicals llc ABT-199 nmr was clearly enriched at the neurite tip, while neurons expressing ΔCAP-Gly p150Glued did not show a similar accumulation (Figure 2C). We quantified this difference using line-scan analysis and showed that wild-type p150Glued is significantly enriched over the

distal 10 μm of the neurite tip as compared to ΔCAP-Gly p150Glued (Figure 2D). These data demonstrate that the CAP-Gly domain functions to properly localize dynactin in the distal neurite. Motors from the kinesin superfamily, including kinesin-1 and kinesin-2, drive the fast axonal transport of vesicular cargos. The anterograde movement of cytosolic proteins via slow axonal transport is also dependent on kinesin-1 (Scott et al.,

2011). We therefore tested whether the distal enrichment of dynactin is dependent on kinesin-1 activity by expressing either the dominant-negative kinesin-1 inhibitor, KHC-tail, or the KHC-stalk, not which does not inhibit the motor and was used as a control (Konishi and Setou, 2009). We found that expression of KHC-tail disrupts the distal localization of dynactin, while expression of KHC-stalk had no effect on dynactin localization (Figure 3A). Line-scan analysis confirmed a significant difference in the distal accumulation of dynactin after expression of the KHC-tail, as compared to localization in neurons expressing either the vector and or the KHC-stalk (Figure 3B). Kinesin-1 has not been shown to directly interact with dynactin, nor did we observe co-immunoprecipitation of the motor with p150Glued expressed in COS7 cells (Figure S3). Thus the mechanism leading to kinesin-1-dependent distal localization of dynactin is likely to be indirect. In contrast, previous work has identified a direct interaction between kinesin-2 and p150Glued (Deacon et al., 2003). Therefore, we tested whether kinesin-2 may also contribute to the anterograde transport of dynactin. Expression of Kif3A-HL, a dominant-negative inhibitor of kinesin-2 lacking the motor domain (Nishimura et al.