This result is in line with the finding that local waves were usually low amplitude, and low-amplitude waves typically occur in late NREM sleep when homeostatic sleep pressure has largely dissipated (Riedner et al., 2007). By contrast, K-complexes were mostly global and stereotypical throughout the night—that is, they did not show significant changes between early and late sleep (Figure 6B; involvement

of 54.8% ± 4.4% in early sleep versus 52.5% ± 1.9% in late sleep; p = 0.98). Interestingly, sleep spindles became slightly less local in late sleep, as sleep pressure dissipated (Figure 6C; involvement selleck products of 44.2% ± 0.6% in early sleep versus 47.1% ± 0.5% in late sleep; p < 0.00014). This result once again supports the notion that local sleep spindles cannot be simply explained by an association with local slow waves. To examine whether slow waves propagate along typical

pathways, we checked for consistent temporal delays between brain regions in which the same wave was observed. Figure 7A provides an example of mean slow waves in depth EEG of different brain structures in one individual, revealing a propagation trend from medial frontal cortex to the MTL and hippocampus. This propagation was evident also when examining the distribution of lags for individual waves (Figure 7A, right). Despite variability in the timings Tariquidar of individual waves, some regions consistently preceded scalp EEG whereas others followed it. A systematic analysis of depth EEG established that slow waves had a strong propensity to propagate from medial frontal cortex to the MTL and hippocampus. Specifically, we identified all slow waves that were detected

within ±400 ms across several brain structures (Experimental Procedures). Sorting regions according the to the order in which their slow waves were detected revealed a clear tendency of slow waves to propagate from medial frontal cortex to the MTL (Figure 7B), which was highly significant statistically (Figure 7C; p < 2.3 × 10−8, unequal variance t test). In addition, this propagation tendency was consistent across individual subjects and robust to different examinations (Figure S6). Figure 7D shows an example of individual slow waves propagating across multiple brain structures. As can be seen, time offsets in OFF periods in different brain regions followed a propagation from frontal cortex to the MTL (diagonal green lines). Next, slow wave propagation was quantitatively examined in unit discharges in all 11 individuals in whom unit recordings were obtained simultaneously in frontal and MTL regions. Mean spiking activities underlying slow waves in medial frontal cortex versus MTL revealed a robust time offset (Figure 7E, left). Across individual neurons, minimal firing in frontal neurons (n = 76) was −85 ± 22 ms relative to scalp Fz negative peak, whereas minimal firing in MTL neurons (n = 155) was +102 ± 20 ms relative to the same time reference, indicating an average difference of 187 ms (Figure 7E, right).

The authors did, however, make an effort to model fast and slowly changing (“phasic” and “tonic”) patterns of LC activity. Whether these patterns relate to the physiology of phasic and tonic firing of LC neurons remains unclear, of course. However, what is remarkable in the present work is that LC activity is specifically modulated by unexpected uncertainty. This specific relationship was predicted by computational modeling (Yu and Dayan, 2005) and behavioral evidence from pupillometry studies (Preuschoff et al., 2011). This fascinating convergence of theory and physiology paves the road AZD9291 concentration for future studies. There are a number emerging questions

which the current study encourages us to tackle. We would like to highlight just two here. The first relates to the exciting possibility to image functional activity in the SN/VTA and LC simultaneously and thus observe the interaction of both regions during decision making. The second relates to the role of the hippocampus in coping with

unexpected uncertainty. As careful but nevertheless inquisitive creatures we balance between drives to exploit what we know and explore Selleck CHIR-99021 the unknown. In so-called “model-free” reinforcement learning, recent reward outcomes are integrated into action-value associations and exploration is undirected (Sutton and Barto, 1998). But the exploration/exploitation dilemma can also be approached from a Bayesian perspective. Decision making in dynamically changing environment improves if the statistics of the environment (model of the world) are tracked to assess the salience of new information and the beliefs about action values are updated accordingly. In such a model-based framework, uncertainty should promote exploration, CYTH4 as supported by some studies (e.g., Badre et al., 2012). On the other hand, human participants tend to avoid uncertain options when ambiguity is high (reviewed by Bach and Dolan, 2012). There are probably different computational mechanisms

that bias the balance toward exploration: one mechanism detects the lack of knowledge in the face of unexpected uncertainty while another mechanism assigns a “bonus” for potential reward to the detected uncertain option or environment, thus favoring their sampling. An intriguing possibility is that these two computational processes depend on two distinct neuromodulatory systems: the noradrenergic system detecting uncertainty and the dopaminergic system assigning bonuses to the uncertain options. The current advances of fMRI now allow us to investigate such hypotheses pertaining to the interaction of the LC and SN/VTA. One remarkable finding is the involvement of the hippocampus in tracking unexpected uncertainty related to reward outcomes. Beyond its association to memory and spatial navigation, the hippocampus, especially its anterior portion, is also related to what is generally known as anxiety response (Fanselow and Dong, 2010).

Our experiment, therefore, was specifically set up to

define the neural mechanisms operating during the emergence of knowledge about social hierarchies, examine how rank information is coded in the brain, and dissociate the operation of social-specific from domain-general processes. Participants completed training trials, where a pair of adjacent items in the hierarchy was presented (e.g., P1 versus P2, G1 versus G2, where P = person and G = galaxy; Figure 1A): they were required to learn through trial and error, which person had more power (social condition) or which galaxy had more mineral (nonsocial condition). Following each block of training trials, participants completed test trials where they were required to select the higher ranking of the two items presented (e.g., P3 versus P6, G3 versus selleck G6; Figure 1B) and rate their confidence in their decision on a scale of 1 (guess) to 3 (very sure). Test trials differed from training trials in two critical ways: nonadjacent items in the hierarchy were presented during test trials (e.g., P3 versus P6), and no corrective feedback was issued. As such, participants were required to use transitive inference to deduce the correct item during test trials (e.g., P3, in a P3 versus P6 trial), by using knowledge of the underlying hierarchy (e.g., P1 > P2 > P3 > P4 > P5 > P6 > P7: see below). In contrast, participants could achieve proficient performance on

training trials by simply memorizing the correct item in each pair (e.g., P1, in a P1 versus www.selleckchem.com/products/PD-0332991.html P2 trial). While the Learn phase paradigm builds on a rich vein of research that has used the transitive inference task across species (Bryant and Trabasso, 1971; Dusek and Eichenbaum, 1997; Greene et al., 2006; Grosenick et al., 2007; Heckers et al., 2004; Hurliman

et al., 2005; Moses et al., 2010; Paz-Y-Miño et al., 2004; Zeithamova et al., 2012), we incorporated several features designed to achieve the specific goals of our experiment: first, we interleaved blocks of training and test trials throughout the time course of the Learn phase in order to chart the development of successful transitive behavior. In contrast, previous fMRI studies have typically included test trials only at the very end of training (Greene et al., 2006; Heckers et al., 2004; Moses et al., 2010). Second, we incorporated a novel measure of test trial performance (i.e., “inference score”), which oxyclozanide was validated in a separate behavioral experiment (see below and Supplemental Results). The inference score index – which incorporated participants’ assessment of their confidence in their choices, a metacognitive measure typically used to characterize medial temporal lobe dependent memory processes (e.g., (Eichenbaum et al., 2007)—allowed us to track the emergence of knowledge of the linear structure of the hierarchy, and thereby reveal the underlying neural mechanisms. Lastly, our paradigm was unique in affording a direct comparison of social (i.e.

Local neurons, in contrast, fired low-amplitude Ca2+ spikes and displayed spike frequency adaptation caused by Ca2+-dependent potassium currents. Fast GABA (LN-PN and LN-LN connections) and nicotinic cholinergic synaptic currents (PN-LN connections) were modeled by first order activation schemes. The equations for all intrinsic and synaptic currents are given in the Supplemental Information and are based on Bazhenov et al., 2001a and Bazhenov et al., 2001b. In Figure 1, Figure 2 and Figure 3 we simulated isolated networks of LNs. The population of LNs and the specific

connectivity are shown in the respective figures. In the following figures we Carfilzomib purchase simulated networks including both excitatory PNs and inhibitory LNs. Drawing from the basic anatomy of the insect AL, the PNs received inputs from LNs and projected random connections back to LNs.

The AL model simulated in Figure 5 included 20 LNs and 100 PNs. LN-PN connections were determined such that each PN occupied a position on the grid in Figure 5. We tested the network with a larger population of LNs and PNs with random connectivity to obtain the same result (propagating waves of activity in the 2D plane). With random connections the population of PNs simulated did not cover all points on the 2D grid. However, the waves of activity could be clearly seen despite gaps in the grid of PNs. We also simulated a network with chromatic number three and were able to generate 2D wave-fronts that propagated along orthogonal directions. Intracellular recordings (Figure 1 and Figure 7) whatever selleck screening library were made from local neurons in adult locusts (Schistocerca americana) obtained from a crowded colony. Animals were immobilized and stabilized with wax with one antenna secured. The brain was exposed, desheathed, and superfused with locust saline as previously described ( Laurent and Davidowitz, 1994). Intracellular electrodes were sharp glass micropipettes (O.D = 1.0 mm, Warner Instruments, 80–230 MΩ, Sutter P97 horizontal puller,

Sutter Instruments) and were filled with 0.5 M potassium acetate and 5% neurobiotin (Vector Laboratories). Data were digitally acquired (5 kHz sampling rate, LabView software and PCI-6602 DAQ and PCI- MIO-16E-4 hardware, National Instruments), stored on a PC hard drive, and analyzed off-line using MATLAB (The MathWorks, Inc.). Odor puffs were dilute grass volatiles delivered as described in Brown et al. (2005). This work was supported by grants from the US National Institute of Deafness and other Communication Disorders (C.A. and M.B.), the US National Institute of Neurological Disorders and Stroke (M.B.) and a US National Institute of Child Health and Human Development intramural award (M.S.). The authors would like to thank Professor Gilles Laurent for many stimulating discussions and insightful suggestions and Stacey Brown Daffron for providing examples of recordings from LNs made in vivo. C.

The transient ZD1839 receptor potential (TRP) channel member TRPV1 is required for the transduction of hyperosmotic stimuli in MNCs (Sharif Naeini et al., 2006) and by osmosensory neurons in the organum vasculosum laminae terminalis

(Ciura and Bourque, 2006). However, osmoregulation still operates in Trpv1−/− mice; thus, other osmosensitive neurons or pathways must be able to compensate for loss of central osmoreceptor function ( Ciura and Bourque, 2006, Sharif Naeini et al., 2006 and Taylor et al., 2008). Osmoreceptors also exist outside the central nervous system (Adachi, 1984, Adachi et al., 1976, Baertschi and Vallet, 1981, Choi-Kwon and Baertschi, 1991, Niijima, 1969 and Vallet and Baertschi, 1982) and these so-called peripheral osmoreceptors could significantly contribute to the regulation of ECF osmolality. However, it is not clear which peripheral neurons function as osmoreceptors and the molecular mechanism by which they detect changes in osmolality is unknown. Much of the Veliparib older work has concentrated on vagal afferent neurons activated by hyperosmotic stimuli (Adachi, 1984 and Niijima, 1969). However, a recent series of studies has provided strong evidence that an autonomic reflex can

be initiated by the activation of peripheral osmoreceptors, specifically by hypo-osmotic stimuli (Boschmann et al., 2003, Boschmann et al., 2007, Jordan et al., 1999, Jordan et al., 2000, Lipp et al., 2005, Raj et al., 2006, Scott et al., 2000, Scott et al., 2001, Shannon et al., 2002 and Tank et al., 2003). Thus, water drinking in man (intake of 500 ml), but also in mice, can initiate an acute pressor

reflex together with increased sympathetic nerve activity and thermogenesis (Boschmann et al., 2007, Jordan et al., 2000, Lipp et al., 2005, McHugh et al., 2010, Scott et al., 2000 and Tank et al., 2003). It has been suggested that there is an osmosensitive sensory system in the liver that signals hypo-osmotic stimuli via the DRG and spinal cord to evoke reflex responses (Tank et al., 2003). Such a peripheral osmosensitive system has not been studied directly in animal models, although there is indirect evidence for its existence (Vallet and Baertschi, 1982 and McHugh et al., 2010). In the present study, we established an animal model in which the activation of Mannose-binding protein-associated serine protease peripheral osmoreceptors could be monitored under realistic physiological conditions. We identified a population of osmosensitive hepatic sensory afferents, which have cell bodies in the thoracic DRG. These neurons can detect very small hypo-osmotic shifts in the osmolality of blood flowing through the liver after water intake. Intriguingly, hepatic sensory neurons possess ionic currents activated by physiological shifts in osmolality; such osmosensitive currents have a pharmacological and biophysical profile similar to the transient receptor channel protein TRPV4.

To compare the importance of rebound depolarizations to those mediated by synaptic excitation, we recorded OFF RGC responses to somatic current injections (see Figure S1 available online). Even for current steps Depsipeptide price (−150 pA) that hyperpolarized OFF RGCs (−102.6 ± 8.3 mV, n = 7 cells) well below the likely reversal potential for inhibitory conductances at this age (Zhang et al., 2006), only two of seven cells fired rebound

spikes. Moreover, when observed, rebound firing gave rise to only few action potentials compared to the robust spike bursts elicited by depolarizing current injections (Figure S1) and observed during waves (Figures 1C and 1D). Responses of ON RGCs to current injections were similar to those of OFF RGCs (Figure S1). Thus, it appears that the offset bursts of ON and OFF RGCs are elicited by sequential excitatory inputs to these neurons, which in the case of ON RGCs outweigh simultaneous inhibitory inputs and in the case of OFF RGCs are preceded by inhibition. Several studies have shown that excitatory input to RGCs during stage III waves is mediated by glutamate and

recent reports identify BCs as its likely source (Blankenship et al., 2009, Firl et al., 2013 and Wong et al., 2000). Selleck Ibrutinib However, how BCs themselves respond during waves is not well understood. To address this question and elucidate the mechanisms that offset excitatory inputs to ON and OFF RGCs, we obtained dual whole-cell patch-clamp recordings from BCs and RGCs

with overlapping neurite territories in P11–P13 retinal flat mount preparations (Figures 2A and 2B). The dendrites of BCs contact either rod (RBCs) or cone (CBCs) photoreceptors. All CBCs (43/43 cells) but no RBCs (0/4 cells) we recorded participated in stage III waves. Like RGCs, CBCs can be grouped into ON and OFF classes. these The axons of ON CBCs stratify in the inner 3/5 of the IPL, those of OFF CBCs in the outer 2/5 where they contact the dendrites of ON and OFF RGCs, respectively (Ghosh et al., 2004). Simultaneous recordings of ON CBCs and ON RGCs revealed that during each stage III wave, ON CBCs depolarize while their membrane potential remains relatively stable between waves (Figure 2C; VRest: −59.4 ± 1.6 mV, n = 27). The timing and shape of ON CBC depolarizations matched those of concurrently recorded ON RGC EPSCs (Figures 2D and 2E; PT: 56 ± 43 ms, n = 18). In contrast, OFF CBCs hyperpolarize during each stage III wave and rest at higher membrane potentials in between (Figure 2F; VRest: −48.4 ± 2.4 mV, n = 16 cells, p < 10−3 for comparison to ON CBCs). The timing of the respective events, similar to depolarizations of ON CBCs, was aligned with the ON phase of each wave (Figure 2G; trough time of cross-correlation: 52 ± 194 ms, n = 10).

We thank Elyssa Margolis for critique of this review. The Sulzer lab’s work on reinforcement-based learning is supported by the NIH and the Picower, McKnight, and Parkinson’s Disease Foundations. “
“Autism spectrum disorders (ASDs) are among the most common neuropsychiatric disorders, with an estimated world-wide prevalence of 1%–2.6% (Kogan et al., 2009 and Kim et al., 2011). Almost 70 years after the description of autism by Leo Kanner and Hans Asperger, tremendous

progress has been made in the recognition and diagnosis of children with ASDs. It is well established that ASDs represent a heterogeneous group of disorders that are highly heritable, with heritability indices estimated at 85%–92%. Advances find more in identifying ISRIB molecular weight the genetic causes of ASDs first came from the study of syndromic autism (ASDs in conjunction with congenital malformations and/or dysmorphic features), which pinpointed the causes of disorders,

such as fragile X syndrome, Rett syndrome, PTEN macrocephaly syndrome, Timothy syndrome, and Joubert syndrome, to name a few ( Miles, 2011). The challenge, however, was identifying the genetic cause of nonsyndromic or idiopathic autism given the lack of defining features besides the neurobehavioral phenotypes and the fact that the majority of cases were simplex (one affected in a family). This issue of Neuron highlights three studies of simplex, mostly nonsyndromic, relatively high-functioning ASDs ( Levy et al., 2011, Sanders et al., 2011 and Gilman et al., 2011), that establish de novo copy-number variants (CNVs) as the cause of 5%–8% of cases of simplex autism. Using different array platforms on practically the same cohort of patients, both Sanders et al. (2011) and Levy et al. (2011) confirmed the role of de novo CNVs in the etiology of idiopathic autism. The analysis of a large number of families from the Simons Simplex Collection (SSC)—887 families in the Levy paper and 1174

families in the Sanders paper—allows them to confirm multiple known ASD loci but also to identify novel loci, such as 16p13.2 Histone demethylase and the CDH13 locus. The sheer number of different de novo CNVs identified in the probands, but not their unaffected siblings, supports the conclusion that autism is mostly caused by rare mutations (at least for CNVs that is), with most de novo events being unique to each proband. As previously established, and now confirmed in larger data sets, deletions and duplications of 16p11.2 are the single most common cause of ASDs identifiable by DNA array analysis. This is the only locus known to date that accounts for > 1% of ASD cases, i.e., 1.1%–1.2%, with deletions being slightly more common than duplications.

05) (Figure 4G). At 15 months of age, BACHD animals are anxious, as measured by failure to explore a lit

arena (light/dark choice, time in light 88 ± 27 s for saline treated BACHD and 248 ± 20 s for nontransgenic animals, p = 0.036). Anxiety was significantly ameliorated in HuASO treated BACHD animals (compared to saline treated BACHD, p = 0.027) and was similar to nontransgenic levels (Figure 4H). Nine months after treatment, human huntingtin levels in ASO-treated animals, measured immediately after the improvement in motor activity, anxiety, and motor coordination was recorded, were comparable to vehicle levels (Figures 4I and 4J). Thus, the improvement in behavior at 15 months came after mutant huntingtin had been restored to its initial level, demonstrating that IWR-1 the beneficial effects of ASO treatment persist for longer than target suppression. Using

an antibody directed against the expanded polyglutamine tract of mutant huntingtin (3B5H10 whose immunogen was a human huntingtin fragment containing 65 glutamines) (Brooks et al., 2004 and Peters-Libeu et al., 2012), a diffuse cytoplasmic staining and pronounced puncta were visible in most striatal cells (including medium spiny neurons) of 15-month-old BACHD mice treated with saline at 6 months of age (Figure 4K, bottom). In contrast, striatum from 15-month-old BACHD, treated at 6 months with HuASO, exhibited only a diffuse staining pattern, similar not to that seen in vehicle treated BACHD brains, but contained very few aggregates (Figure 4K, middle). No aggregates or diffused staining were observed Vorinostat molecular weight in nontransgenic brains (Figure 4K, top). Thus, despite

the restoration of soluble mutant protein levels 9 months posttreatment (Figure 4J), transient suppression of mutant huntingtin was sufficient to delay the formation of polyglutamine aggregates, and the delay lasted longer than the reduction of the soluble mutant protein. To determine if suppression of endogenous, wild-type huntingtin attenuates the benefits of lowering mutant huntingtin and to determine if normal huntingtin can safely be lowered in adult animals, BACHD and nontransgenic littermates were treated at 2 months of age (Figure 5A) with vehicle, the mutant human huntingtin selective ASO that does not alter normal mouse huntingtin (HuASO) (Figure S1A) or an ASO that reduces mutant huntingtin to the same level as the HuASO while simultaneously lowering normal mouse huntingtin to 75% normal levels (MoHuASO) (Figures 1F–1H). At treatment, 2-month-old BACHD animals already exhibit impaired motor coordination (before treatment the latency to fall of saline treated BACHD mice is 142 ± 11 s and nontransgenic animals is 197 ± 10 s, p = 0.013) (Figure 5B, top; see also Figure S5 for all p values). Selective suppression of mutant huntingtin (HuASO) improved motor coordination 3 months after treatment (5 months of age; p = 0.

(2006). AP-Sema6D-Fc, AP-Sema6A, or AP-Sema6C (gift of H. Fujisawa, Nagoya University) was transfected into HEK293 cells, and the protein was purified from culture supernatants. To assess binding, HEK293 cells were transiently transfected with expression vectors encoding Plexin-A1 (gift of A.W. Püschel), Neuropilin-1 (gift of R.J. Giger, University of Michigan), Nr-CAM, L1 (gift of D. Felsenfeld, Mount Sinai School of Medicine), MG-132 solubility dmso TAG-1 (gift of A. Furley, University of Sheffield), or

Neurofascin 186 (gift of V. Bennett, Duke University). AP-fusion protein binding to tissue sections was performed as described previously by Yoshida et al. (2006). All data were analyzed, and graphs were constructed using OpenLab imaging software, MetaMorph software,

or Microsoft Excel. All error bars represent the SEM, and statistical analysis was determined using one-way ANOVA followed by the Tukey’s post hoc test, where appropriate. In each figure the asterisk (∗) indicates p < 0.01, and N.S. indicates not significant (p > 0.05). We thank members of the C.M. lab, Jane this website Dodd, Jon Terman, and Alex Kolodkin for helpful comments on the experiments and manuscript. This work was supported by National Institutes of Health Grants EY12736 (to C.M.) and NS065048 (to Y.Y.), the Howard Hughes Medical Institute (to T.M.J.), Uehara Foundation (to T.K.), Ministry of Health, Labour and Welfare, Program for Promotion of Fundamental Studies in Health Sciences of the National Institute of Biomedical Innovation, and Target Protein Research Program of the Japan Science and Technology Agency (to A.K.), and Ministry of Education, Culture, Sports, Science and Technology of Japan, and the Japan Society for the Promotion of Science (to N.T.). “
“Early in development neurons make far more synaptic connections

than are maintained in the mature brain. Synaptic pruning is an activity-dependent developmental program in which a large number of synapses that form in early development are eliminated while a subset of synapses are maintained and strengthened (Hua and Smith, 2004, Katz and Shatz, 1996 and Sanes and Lichtman, 1999). While it is clear that neuronal activity plays a role, the precise cellular and molecular mechanisms underlying this developmental process remain to be elucidated. Microglia are the resident CNS immune cells which have long been recognized as rapid responders Terminal deoxynucleotidyl transferase to injury and disease, playing a role in a broad range of processes such as tissue inflammation and clearance of cellular debris (Hanisch and Kettenmann, 2007, Kreutzberg, 1996 and Ransohoff and Perry, 2009). In contrast to disease pathology, the function of microglia in the normal, healthy brain is far less understood. However, recent studies suggest that microglia may play a role in synaptic remodeling and plasticity in the healthy brain (Davalos et al., 2005, Nimmerjahn et al., 2005, Paolicelli et al., 2011, Schafer et al., 2012, Tremblay et al., 2010a and Wake et al., 2009).

, 2007). Since FK506 affects diverse signaling pathways in many cell types, it may act directly on neurons or influence the neuronal environment by modulating glial activation. Inhibition

of tau aggregation may also be mediated by direct binding of tau to the FK506 binding protein 52 (Chambraud et al., 2010). As discussed above, it is far from certain that filamentous tau is actually toxic. Indeed, it is not known which tau assembly or conformation is responsible for tau-dependent neuronal dysfunction and degeneration. Not surprisingly, it is equally uncertain whether the abundance of this entity is lowered by any of the available tau aggregation blockers. In fact, some tau aggregation inhibitors enhance the formation of potentially toxic tau oligomers (Taniguchi et al., GSK1210151A ic50 2005). This scenario is reminiscent of the current state of anti-Aβ treatment, where it is also unclear whether any of the anti-Aβ strategies that have undergone or are currently in clinical trials significantly reduce the abundance of Aβ oligomers in human brain tissues, which are suspected to be the main mediators of Aβ-induced neuronal dysfunction (Ashe and Zahs, 2010, Cheng et al., 2007, Sakono and

Zako, 2010 and Shankar et al., 2008). In mice, partial reduction of tau during early development is well tolerated, increases resistance to chemically induced seizures, and markedly diminishes Aβ- and ApoE4-induced neuronal and cognitive impairments in vivo (Andrews-Zwilling et al., Hormones antagonist 2010, Ittner et al., 2010, Roberson et al., 2007 and Roberson et al., 2011). Assuming ongoing experiments confirm that reduction of overall tau levels is efficacious and safe also when initiated in adult and old animals with AD-related pathologies, tau could be targeted directly Oxalosuccinic acid with RNAi approaches in patients with AD. Alternatively, tau levels could be reduced indirectly by targeting molecules

that regulate the expression or clearance of tau. Tau is thought to be degraded via the ubiquitin-proteasome and lysosomal pathways. The ubiquitin ligase for tau was identified as the C terminus of HSP70-interacting protein (CHIP) (Hatakeyama et al., 2004, Petrucelli et al., 2004 and Shimura et al., 2004). Reduction of CHIP levels increased the accumulation of tau aggregates in P301L human 4R0N tau mice (JNPL3 model), and CHIP levels are reduced in AD brains (Sahara et al., 2005). Furthermore, as its name suggests, CHIP works in combination with heat shock proteins to regulate tau degradation (Dickey et al., 2007); levels of heat shock protein 90 (Hsp90) correlate inversely with the levels of soluble tau and tau oligomers (Sahara et al., 2007b). In AD brains, tau is hyperacetylated, which should increase its half-life (Min et al., 2010), alter its microtubule binding and enhance aggregation (Cohen et al., 2011).