Most of the complexity derives from the fact that, because the benefits of information are only indirect, computing its value requires planning across a sequence of steps. Moreover, this planning requires not only a simple knowledge of the order of various steps, but a sophisticated model of the task structure that specifies the hidden (causal) relationships between consecutive steps. Consider for example the simple act of directing gaze to the water faucet while preparing

a tea (Figure 2A). To generate this apparently trivial act, the brain must know not only that the faucet is associated with the task (after all, so are the kitchen floor and the walls) but that lifting the handle will cause the water to flow, which in turn will have http://www.selleckchem.com/products/Rapamycin.html selleck kinase inhibitor a determining influence on preparing the tea. In other words, to determine which sources of uncertainty should be optimally resolved, the brain must know which steps are causal or predictive of the future outcome ( Gershman and Niv, 2010). In a simple scenario such as making a tea this computation may be greatly aided by extensive practice. In other behaviors, however, it requires much more difficult inferences on longer time scales. It can be prohibitively complex for example, to determine which one of the available stimuli is informative if one lands on Mars, or which economic

indicator is truly consequential for a future outlook. Converging evidence shows that humans indeed infer hidden models of complex tasks (Acuña and Schrater, 2010; Braun et al., 2010; Daw et al., 2011; Gershman and Niv, 2010;

Yakushijin and Jacobs, 2011), and indirect evidence from tasks involving schemas or contextual associations suggests that lower animals may also possess this capacity (Balan and Gottlieb, 2006; Braun et al., 2010; Johnson et al., 2012). Building internal models that identify the relevant steps is critical for specifying what subset of a very high-dimensional STK38 information stream should be considered at a given time. Such models, in other worlds, are necessary for deciding to what to attend. As mentioned above in relation with the associability equation (Equation 2), this process entails an executive mechanism that learns how to learn—that is, decides how to organize the moment by moment sampling of sensory information. The need for hierarchical learning has been discussed in relation to motor control and cognitive tasks ( Braun et al., 2010; Johnson et al., 2012) and, as it is clear from this discussion, is also at the heart of attention control. Given an appropriate model of a task structure, informative options (stimuli or actions) may be identified through a prediction error mechanism as those options which, by reducing uncertainty, increase the expected future reward.

30%–40%), where dense local connectivity (Figure 1) and the massive bolus of postsynaptic activity induces high spiking rates (Figure 2). Finally, we found IPSCs

to contribute approximately 10% of the total (excitatory and inhibitory) synaptic contribution, i.e., under the conditions studied here excitatory input dominates the synaptic contribution. Temporal frequency (“1/f”) and distance (“1/r”) scaling of LFP signals can reveal aspects of neural processing (Bédard et al., 2006, Katzner et al., 2009, Miller et al., 2009, Milstein et al., 2009, Pritchard, 1992 and Rasch et al., 2009). Which sort of scaling do Selleckchem INCB018424 our simulations exhibit? Using the Ve traces recorded in depths ranging from 500 to 1,700 μm (representative Ve traces shown in Figure 8A; blue: PSC only, black: passive membranes, red: active membranes),

Anti-diabetic Compound Library datasheet we initially calculated the power spectral density (PSD) P (“control” simulations in Figure 8B; line: mean, shaded area: SD). We calculate the best fit (see Table S2) to P(f) ∝ 1/fα with f being the frequency and α the scaling exponent for two bandwidths: <40 Hz ( Figure 8C, bottom) and 40–1,000 Hz ( Figure 8C, top). α is consistently smaller across all cases of input correlation for low frequencies compared to high ones (circles: mean; error bars: SEM), with the differences in α between all cases being small for <40 Hz ( Table S3). For 40–1,000 Hz, α is similar between PSC and passive membrane simulations, while substantially reduced for active membranes ( Table S3). For example, for the “control” simulation with active membranes, α = 2.0 ± 0.4, whereas for passive membranes, α = 3.7 ± 0.1. (For <40 Hz, for the “control” simulation, α = 1.0 ± 0.2 and 0.9 ± 0.1, respectively.) Notably, experimental recordings

exhibit α close to two ( Miller et al., 2009 and Milstein et al., 2009), with α smaller at lower frequencies ( Miller et al., 2009). We conclude that α is crucially shaped not only by postsynaptic currents but also by membrane characteristics in the 40–1,000 Hz range. How do individual Cell press neurons and the associated microvariables give rise to such frequency-scaling evident in the macrovariables, i.e., the LFP? To address this question, we defined a single-cell frequency scaling exponent for all L5 pyramidal neurons (the population with the strongest LFP contribution), where P(f) ∝ 1/fβ, and calculated the mean Ve of all 5,364 L5 pyramidal neurons at three different locations relative to the soma ( Figures 8D and 8E shows the “control” simulation). The PSD as well as its frequency scaling differs substantially depending on whether only PSC, passive cable structures, or active membranes contribute to the LFP. PSC and passive membranes consistently give rise to steeper scaling and larger β (approx. 2.5–3; Figures 8E and 8F; Table S4) for all simulations, whereas for active membranes β is smaller (approx. 1–2; Table S4).

This will need to be tested as other antiobesity neurons are identified. Given

the above-mentioned findings, GABAergic output appears to be an important, direct target of leptin action. By using leptin-inducible P-STAT3 and GFP reporter expression (in Vgat-ires-Cre mice) to colocalize LEPRs and GABAergic neurons, we observed that LEPR-expressing GABAergic neurons are located in the arcuate, the DMH, and the lateral hypothalamus. Consequently, LEPR-expressing GABAergic neurons in one, two, or all three of these sites mediate leptin’s EX 527 clinical trial antiobesity effects. At present, our results do not allow us to rule in or out any one site or any combination of these sites. Nevertheless, for reasons listed below, we favor an important role for neurons in the arcuate. First, LEPR-expressing arcuate neurons have unparalleled access to circulating leptin ( Faouzi et al., 2007). Second, the arcuate has many GABAergic neurons, a small fraction of which are AgRP neurons ( Figure 3C and Acuna-Goycolea et al., 2005, Horvath et al., 1997 and Ovesjö et al., 2001). Third, POMC neurons, which are key targets of leptin-responsive GABAergic neurons ( Figure 5, Figure 6 and Figure 7

and BYL719 manufacturer Cowley et al., 2001), are located within the arcuate, surrounded by a dense population of GABAergic neurons ( Figure 3A). Fourth, neurons in the arcuate make many local connections ( Matsumoto and Arai, 1978 and van den Pol and Cassidy, 1982), providing the apparatus for local regulation of POMC neurons. One previously defined local circuit, likely to be physiologically important, is that between AgRP neuron collaterals and POMC neurons ( Cowley et al., 2001 and Horvath et al., 1992). As discussed below, we postulate that this is just one of many local leptin-responsive GABAergic inputs to POMC neurons. An earlier study (Cowley

et al., 2001) established that leptin reduces the frequency of IPSCs in POMC neurons (25% reduction in one-third of POMC neurons). The source of the reduced GABAergic input was attributed to AgRP neurons (which also express NPY). In the present study, we confirm leptin’s inhibitory effect on IPSC frequency, but, of interest, note a larger effect (40% inhibition in of all POMC neurons), perhaps because of our use of thicker brain slices (300 μm versus 200 μm). A key outcome of that prior study was the compelling proposal that leptin indirectly regulates POMC neurons via AgRP/NPY-GABAergic collaterals. The degree to which this accounts for leptin’s antiobesity effects, however, has been unclear, especially because deletion of LEPRs from AgRP neurons produces only a small disturbance in energy balance (van de Wall et al., 2008). In the present study, we show that the above-mentioned effect (i.e.

In a panel of iPSC-derived dopamine neurons from PD patients with mutations in either LRRK2 or PINK1, the kinase inhibitor GW5074 was reported to protect cultures from the toxicity of valinomycin (a potassium ionophore that induces oxidative stress and thus may mimic

environmental stressors in vivo [Cooper et al., 2012]). In AD patient iPSC-derived cortical neurons that harbor duplication of the APP locus, β-secretase but not γ-secretase inhibitors were found to suppress an altered TAU phosphorylation phenotype ( Israel et al., 2012). A histone acetyltransferase inhibitor, anacardic acid, was reported to be protective in the context of TDP-43 mutant iPSC-derived motor neurons treated with the neurotoxin arsenite ( Egawa et al., 2012); anacardic acid was chosen on the basis of its potential to modify gene expression changes observed in the mutant cells.

It will be important to further validate Selleck NVP-BGJ398 these candidates therapeutics in multiple independent cell cultures. Phenotypic analyses of functional neuronal parameters—such as membrane excitability or synaptic connectivity—have thus far been limited, in Capmatinib the context of reprogramming-based models of neurodegeneration. Recent studies using iPSC-derived neurons in the context of psychiatric disorders, such as schizophrenia (Brennand et al., 2011) and Timothy syndrome (Paşca et al., 2011 and Yazawa and Dolmetsch, 2013), have considered such functional neuronal parameters, and attempted to use these analyses in the pursuit of therapeutics.

In iPSC-derived cortical neuron cultures from schizophrenia patients and unaffected controls, synaptic connectivity was evaluated in terms of the trans-synaptic spread of a modified, fluorescently tagged rabies virus ( Brennand et al., 2011). Such synaptic connectivity appeared reduced in the schizophrenia patient iPSC-derived neurons, relative to iPSC-derived neurons from unaffected individuals. Further studies are needed to determine whether this observation can be generalized to independent patient cohorts with schizophrenia, and with respect to its utility in screening potential drugs ( Brennand et al., 2011). The different reprogramming-based neuronal models discussed above may have unique either virtues or limitations in the context of drug screens. iPSC-based models allow for extensive expansion of cells, and thus may be beneficial in a broad high-content screen. A method developed to further facilitate the use of iPSC in high-content drug screens enables the expansion and maintenance of iPSC-derived neural progenitors (Koch et al., 2009 and Li et al., 2011; Reinhardt et al., 2013). In contrast to iPSC technology, high-content screening with direct reprogramming-based models requires expansion of the source fibroblast cultures, which is limited by senescence. The use of iNSC technology, as detailed above, may combine the advantages of these two approaches.

Consistent with the action of Homer1a to compete with Homer1c, Homer1a expression reduced the coimmunoprecipitation (co-IP) of mGluR5 with Homer1c compared to GFP-expressing neurons (Figure 4F). We monitored Homer1a mRNA and protein in DIV 14 cortical neurons treated with TTX or bicuculline for 3 hr, 6 hr, 12 hr, 24 hr, and 48 hr (Figure 5A). check details Bicuculline produced a time-dependent increase that was maximum at 6hrs for mRNA and 12 hr for protein (each ∼8-fold), and returned to basal levels at 48 hr. By

contrast, TTX treatment reduced Homer1a mRNA ∼5-fold by 24 hr and protein by ∼4-fold at 48 hr. To assess how Homer1a KO affects homeostatic scaling, we examined the surface levels of AMPARs after chronic TTX or bicuculline treatment. Biotinylation and IHC assays revealed an absence of homeostatic adaptations of GluA2/3 in Homer1a KO neurons (Figures 5B–5E). Homeostatic adaptations of GluA1 were significantly reduced in Homer 1a KO neurons, but not as strikingly disrupted as GluA2. Homeostatic adaptations of mGluR5 were not significantly different in Homer1a KO neurons. Disruption

of homeostatic scaling in Homer1a KO neurons was also evident in mEPSCs recordings (Figures 5F and 5G). In contrast to WT neurons where TTX resulted in an increase of mEPSC (WT-control BMS-754807 order 20.9 ± 1.1 pA; n = 24 cells; TTX-treated 30.1 ± 2.2 pA; n = 15 cells, ∗∗∗p < 0.001), mEPSC amplitudes of TTX-treated Homer1a KO neurons

(31.4 ± 2.6 pA; n = 20 cells) were not significantly greater than untreated Homer1a KO neurons (28.9 ± 1.3 pA; n = 33 cells) (Figure 5G). Similarly, chronic bicuculline treatment reduced mEPSC amplitudes in WT neurons (14.1 ± 0.2 pA; n = 28 cells; ∗∗∗p < 0.001), but did not produce a significant decrease in mEPSC amplitudes in Homer1a KO neurons (27.2 ± 1.9 pA; n = 35 cells) compared to untreated Homer1a KO neurons. Comparison of Homer1a KO neurons treated with bicuculline versus TTX suggested a small difference but was not statistically significant mafosfamide (27.2 ± 1.9 pA compared to 31.4 ± 2.6 pA; p = 0.19, not significant); this is dramatically different than WT neurons (14.1 ± 0.2 pA compared to 30.1 ± 2.2 pA). There was no difference in the frequency of mEPSCs between TTX-treated WT neurons (24.4 ± 2.6 Hz; n = 24 cells), bicuculline-treated WT neurons (22.2 ± 1.7 Hz; n = 28 cells), untreated WT neurons (23.4 ± 2.6 Hz; n = 24 cells), or similarly treated Homer1a KO neurons (TTX-treated, 24.9 ± 2.6 Hz; n = 20 cells; bicuculline-treated 27.6 ± 2.8 Hz; n = 35 cells; untreated 25.3 ± 2.9 Hz; n = 33 cells) (Figure 5G). These observations confirm that homeostatic changes of synaptic strength are markedly disrupted in Homer1a KO neurons.

To avoid bleed-through of stimulus light into the detection pathway of the microscope, the built-in red, green, and blue LEDs of the microprojector were externally supplied by a custom-built power source that was synchronized to the fly-back interval of the laser beam at the end of each line, during which no fluorescence data were acquired (fly-back width 0.33 ms, line frequency 864 Hz, 28.5% duty cycle). This generated a virtually flicker-free stimulus sequence. The animal was UMI-77 chemical structure positioned with the right eye facing the center of the projection area, which covered ∼90° of the visual field. The stimulus was a white bar on dark background, moving at a speed of 35°/s, in four or eight directions evenly spanning

360°. For whole-cell recordings in the tectum, the skin overlying the midbrain was cut with an etched tungsten needle and removed with fine forceps. The extracellular recording solution contained 134 mM NaCl, 2.9 mM KCl, 2.1 mM CaCl2, 1.2 mM MgCl2, 10 mM HEPES, and 10 mM glucose; pH 7.8/290 mOsm/kg. Patch pipettes were pulled from borosilicate glass (Hilgenberg, outer diameter 2 mm, inner diameter 1 mm) and filled with internal solution BMS-354825 chemical structure (125 mM K-gluconate, 10 mM HEPES, 10 mM EGTA, 2.5 mM MgCl2, 4 mM ATP-Na, and 0.3 mM GTP; pH 7.3/285 mOsm/kg). In some experiments, K-gluconate was replaced with Cs-gluconate to minimize voltage-gated and leak potassium conductances during voltage-clamp recordings. To analyze neuronal morphology, we

added sulforhodamine-B or Alexa Fluor 594 hydrazide (both 360 μM; Invitrogen) to the internal solution. Open tip resistance was 7–9 MΩ. Input resistance of tectal neurons was 2.8 ± 0.3 GΩ (n = all 19). Patch-clamp recordings were performed using a Multiclamp 700B amplifier (Molecular Devices). Signals were filtered at 3 kHz and recorded at 10–20 kHz using a PCIe-6251 board and custom-written LabVIEW data acquisition software (version 8.6, National Instruments). To isolate excitatory and inhibitory synaptic currents, we voltage clamped cells at −60mV (close to the reversal potential of GABA receptor channels) and 0mV (close to the reversal potential of glutamate receptor channels), respectively. Cell spiking

was recorded in the cell-attached mode or in the whole-cell current-clamp configuration. During current-clamp recordings, small hyperpolarizing current was injected in some cases to keep the cell at a resting potential near −60mV. In current clamp, action potentials were often small but could clearly be detected as spikes by taking the derivative of the voltage trace due to the fast depolarization of the membrane potential at spike onset. We thank W. Denk for generous support and helpful discussions. We thank B. Knerr, M. Glöck, and A. Wolf for their contribution in generating transgenic lines. We thank A. Borst, W. Denk, S. Preuss, and F. Svara for comments on an earlier version of the manuscript, J. Tritthardt and C. Kieser for expert help with electronic design, M. Lukat and N. Neef for mechanical design, M.

Under this scenario, improving

the quality of inference performed by the network results in smaller correlations as long as the tuning curves remain the same (Bejjanki et al., 2011). Again, this is by no means a general rule. If the tuning curves change as a result of making an approximation less severe, it is in fact possible to decrease uncertainty while increasing correlations. In summary, the relationship between suboptimal inference and neural variability is complex. With population codes, suboptimal inference increases uncertainty by reshaping the correlations or the tuning curves or both. Suboptimal inference may also have an impact on single-cell variability, but in large networks, changes in single-cell variability alone have only a minor impact on behavioral performance. Recently, Osborne et al. (2005) argued that 92% of the behavioral PLX 4720 variability in smooth pursuit is explained by the variability in sensory estimates of speed, direction, and timing, suggesting that very little noise is added in the motor circuits controlling smooth pursuit. If one were to build a model of smooth pursuit, a natural way to capture these results would be to inject a large amount of noise into the networks BMS354825 prior to the visual motion area MT and very little noise thereafter. Although

this is possible, it is a strange explanation: why would neural circuits be noisy before MT but not after it? We propose instead that most of the uncertainty (in this case, the variability in the smooth pursuit) comes from suboptimal inference and that suboptimal inference is large on the sensory side and small on the motor side. This would explain the Osborne et al. (2005) finding without having to invoke different levels of noise in sensory and motor circuits. And it is, indeed, quite plausible. MT neurons are unlikely to be ideal observers

of the moving dots stimulus used in their study; they are more likely tuned to motion in natural images. Therefore, the approximations involved in processing the dot motion will result in large stimulus uncertainty in MT. By contrast, it is quite possible that the smooth pursuit system is near optimal. Indeed, the eyeball has only 3 degrees of freedom and it is one of the simplest and most also reliable effectors in the human body (it is so reliable that proprioceptive feedback plays almost no role in the online control of eye movements; Guthrie et al., 1983). If this explanation is correct, these results could be modified by comparing performance for two stimuli that are equally informative about direction of motion, but for which one stimulus is closer to the optimal stimulus for MT receptive fields. We predict that the percentage of the variance in smooth pursuit attributable to errors in sensory estimates would decrease when using the near-optimal stimulus. By contrast, if the variance of the sensory estimates is dominated by internal noise, such a manipulation should have little effect.

, 1989, DeLong, 1990, Graybiel, 1995, Hikosaka et al., 2000, Kravitz et al., 2010 and Mink, 1996). The coordinated activity of these two output streams is thought to be critical for learning and performing proper action sequences. Although the two projection cell classes in dorsal striatum, known

as medium spiny neurons (MSNs), are intermingled, they can be distinguished by their gene expression and by their downstream AZD2281 projection targets (Beckstead, 1987, Chang et al., 1981, Gerfen et al., 1990, Kawaguchi et al., 1990, Le Moine et al., 1990, Penny et al., 1986 and Smith et al., 1998). Direct-pathway MSNs express the dopamine D1 receptor, and project primarily to pars reticulata of substantia nigra (SNr), as well as sending strong inputs to the entopeduncular nucleus (EP), the rodent homolog of the internal portion of globus pallidus. Indirect-pathway BTK inhibitor MSNs express the dopamine D2 receptor and send their primary projections to the globus pallidus (GP, external portion in primates). Activation of direct or indirect pathways yields opposing effects on movement, reinforcement,

and reward-related behaviors (Ferguson et al., 2011, Hikida et al., 2010, Kravitz et al., 2010, Kravitz et al., 2012 and Lobo et al., 2010). Although the gross anatomy of striatal input has been thoroughly studied through use of traditional tracers (Bolam et al., 2000, Gerfen, 1984, Graybiel and Ragsdale, 1979, McGeorge and Faull, 1987, Pan et al., 2010, Ragsdale and Graybiel, 1981 and Schwab et al., 1977), these techniques cannot distinguish inputs to specific cell types, nor can they separate synaptic from extrasynaptic input. Montelukast Sodium Moreover, they can often label fibers of passage. Electron microscopy (EM) studies have found some preliminary evidence that input bias into the dorsal striatum may exist (Lei et al., 2004), but these

data can only sample small numbers of synapses in a restricted volume of tissue. We wished to overcome these limitations by utilizing newly developed genetic tools to dissect the inputs to MSN subtypes in dorsal striatum with single cell resolution, at the whole brain level. We sought to determine whether information segregation in the basal ganglia arises at the level of the MSNs in the striatum or whether these two pathways receive asymmetric input that could differentially regulate the activity of one pathway versus the other. These data could provide a starting point for assessing how distinct striatal inputs shape the functional roles of the direct and indirect pathways. We utilized pathway-specific Cre driver lines (Gong et al., 2007), combined with a recently described technique that allows us to target specific cell types and label their monosynaptically connected inputs (Wall et al., 2010). We then quantified the relative input strengths from brain regions that project directly onto direct- or indirect-pathway MSNs in a central region of dorsal striatum.

In contrast to I287, increasing the hydrophobicity of V363 stabilizes the resting versus active VS conformation.

Hence, the endogenous Thr present at the homologous position in the VS of Nav DI–DIII destabilizes the resting state relative to the activated state, consequently reducing the energy barrier underlying VS activation (Figure 4E). This mechanism agrees well with previous works showing that the replacement of the native residues intercalated between the Shaker S4 Arg by less hydrophobic amino LY294002 supplier acids destabilizes the resting versus the depolarized VS conformation (Xu et al., 2010). Several molecular dynamics simulations of the resting conformation of the Kv1.2 voltage sensor show that the side chain of the residue homologous to V363 points toward the lipid

bilayer (Delemotte et al., 2011, Henrion et al., 2012, Jensen et al., 2010, Khalili-Araghi et al., 2010, Lacroix et al., 2012 and Vargas et al., 2011). In the VS resting state, this residue is therefore probably surrounded by the hydrophobic environment of the lipid bilayer and completely buried from the solvent (Figure 4F). Hence, this VS conformation will be energetically more stable when this residue bears a hydrophobic side chain and conversely will be less stable when this side chain is made more hydrophilic (Figure 4E). Interestingly, the presence MI-773 purchase of two hydrophilic residues in S4-DIII, one after K1 and one after R2 (Figure 2A and Figure S2), may constitute the molecular basis to account for the earlier activation-onset of domain III during sodium channel activation (Chanda and Bezanilla, 2002 and Gosselin-Badaroudine et al., 2012). The mutation V363I produces the largest positive Q-V

shift. Interestingly, the homologous mutation T220I in S4-DI of Nav1.5, a cardiac-specific Nav channel, is associated with early development of dilated cardiomyopathy (Olson et al., 2005). Figure S5 shows that the T220I mutation produces a positive shift of approximately +10 mV for both the channel’s availability and open probability, in agreement with the V363I phenotype. The proposed mechanism for the S4 speed-control site was further tested by conducting similar experiments Vasopressin Receptor in the unrelated VS from the Ciona Intestinalis voltage-sensitive phosphatase (Ci-VSP). Figure 5 shows that decreasing the hydrophobicity of the side chain at position L224, homologous to V363 in Shaker, negatively shifted the Q-V curve and accelerated the activation kinetics but did not significantly alter deactivation kinetics. Thus, similar mutations of this residue produce similar effects in two evolutionary-distant VSs. From the point-of-view of evolution, it is tempting to hypothesize that the rapid VSs that characterize Nav channels were designed by natural selection during the development of nervous systems.

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 check details 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 selleck kinase inhibitor 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 no 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).