This system allowed us to monitor synaptic transmission in the sa

This system allowed us to monitor synaptic transmission in the same neuron before and after selective Thr-induced cleavage of NLG1. Recordings from neurons expressing GFP-NLG1 lacking a Thr cleavage site were used to control for possible off-target effects of Thr activity. We first examined the effect of NLG1 cleavage on miniature excitatory postsynaptic currents (mEPSCs; Figure 6A).

Application of Thr for 30 min reduced mEPSC frequency (44% ± 10% of baseline; Figures 6B and S6A) with no significant effect on amplitude (91% ± 8% of baseline; Figures 6C and S6A), indicating that NLG1 ectodomain cleavage does not change postsynaptic AMPA receptor number or function, but rather reduces neurotransmitter release or decreases the number of functional excitatory synapses. By contrast, Thr had no effect on mEPSCs in neurons expressing GFP-NLG1 (mEPSC frequency, 100% ± GDC-0068 chemical structure 3% of baseline; amplitude, 102% ± 4% of baseline; Figures 6A–6C and S6B). Importantly, there was no difference in baseline mEPSCs between neurons expressing GFP-NLG1 (frequency: 1.74 ± 0.44 s−1; amplitude: 11.66 ± 1.70 pA) and GFP-Thr-NLG1 (frequency: 2.00 ± 0.36 s−1; amplitude: 10.74 ± 0.61 pA). Consistent

with the predominant localization of NLG1 to glutamatergic synapses (Chubykin et al., 2007; Graf et al., 2004), NLG1 cleavage failed to alter miniature inhibitory postsynaptic current (mIPSC) frequency MycoClean Mycoplasma Removal Kit or amplitude (mIPSC frequency: this website 103 ± 3%; mIPSC amplitude: 98% ± 8% of baseline; Figures S6C–S6F), indicating that NLG1 cleavage does not acutely alter GABAergic transmission. The reduction in mEPSC frequency (Figures 6A, 6B, and S6A) together with the destabilization of NRX1β (Figures 5C–5G) suggested a possible alteration in presynaptic function. To test this further, we examined the effects of Thr-induced NLG1 cleavage on evoked excitatory synaptic currents (eEPSCs) and their paired-pulse responses.

Evoked responses were elicited by stimulating nearby cells with an interstimulus interval of 100 ms. Neurons expressing GFP-NLG1 or GFP-Thr-NLG1 exhibited similar baseline paired-pulse ratio (PPR) responses (GFP-NLG1: 1.42 ± 0.12; GFP-Thr-NLG1: 1.59 ± 0.46). Recordings were then made on the same cell before and 30 min after thrombin application. Thrombin treatment of neurons expressing GFP-Thr-NLG1 reduced the amplitude of the first response and increased PPR (eEPSC amplitude: 50% ± 18% of baseline; PPR: 139% ± 8% of baseline; Figures 6D–6F), strongly suggesting that NLG1 cleavage decreases the probability of neurotransmitter release. This effect was not attributable to Thr treatment per se, as there was no change in eEPSC amplitude or PPR in neurons expressing GFP-NLG1 lacking a Thr cleavage sequence (eEPSC amplitude: 95% ± 8% of baseline; PPR: 106% ± 6% of baseline; Figures 6D–6F).

The excitatory input to PV1 cells did not show a discontinuous de

The excitatory input to PV1 cells did not show a discontinuous decrease in strength (Figure 4D), suggesting that horizontal cells are not responsible for the switch. Since amacrine cells mediate inhibitory input to ganglion cells, we conclude that the switch involves the activation of GABAergic spiking amacrine cells that can act from a distance and are directly connected to PV1 cells. To confirm that far reaching amacrine cells directly connect to PV1 cells, we carried out monosynaptically restricted viral tracing using G-deleted rabies virus in which the G protein is supplied to the PV ganglion cells by a conditional adeno-associated

PD0332991 concentration (Marshel et al., 2010; Stepien et al., 2010; Wickersham et al., 2010) or Herpes virus (Yonehara et al., 2011) (Figure S6). We reconstructed Autophagy inhibitor the transsynaptically labeled amacrine cells around three PV1 cells, each in a different mouse (Experimental Procedures), and found amacrine cells with long processes, some reaching over 1 mm across the retina, connected to PV1 cells (Figures 5, S6, and S7). These “wide-field” amacrine cells, revealed by monosynaptic tracing, are probably the inhibitory cells that are activated by the switch. Note that PV cells other than PV1 also receive input from wide-field

cells and, therefore, the PV1 connecting amacrine cells must have special properties that allow the implementation of the switch (Lin and Masland, 2006). How could inhibition be differentially activated in two different regimes of vision? The retina incorporates two kinds of photoreceptors, rods and cones, which provide the sensory interface for image-forming vision. The more sensitive rods and the less sensitive cones have overlapping light intensity ranges of signaling (Figure S2) and, therefore, three ranges can be defined: vision mediated by rods only, rods and cones, and cones only. In order to determine whether the transition between switch-OFF and switch-ON states corresponds to the transition

from vision mediated by rods only to rods and cones, or rods and cones to cones only, we recorded from rod and positive contrast-activated cone bipolar cells in a retinal slice preparation (Figures 6A–6C). We presented the slice with full-field steps of illumination with fixed contrast across different light intensities, Casein kinase 1 incorporating rod only and cone only intensity ranges. The critical light intensity at which the switch was turned on corresponded to those light intensity values in which cone bipolar cells became strongly activated. At this light intensity, rod bipolar cells have already been fully activated. The critical light intensity was within the range reported to activate cones in mice (Nathan et al., 2006; Umino et al., 2008). These experiments are consistent with a view that the activation of cones toggles the switch (see Discussion for an alternative explanation).

For each session, functional images were realigned to the first v

For each session, functional images were realigned to the first volume in the time series to correct for motion and coregistered to the T2-weighted structural image from the corresponding scan session. To coregister images across the two scanning sessions, the T2-weighted structural images from each session were coregistered to the T1 SPGR image,

and the coregistration parameters were applied to the corresponding functional images from the same session. Functional images were then resliced to the space of the mean functional image from the second session, high-pass filtered (128 s), and converted to percent signal. All analyses were performed in the native space of each participant; PF-01367338 nmr no spatial smoothing was applied. Pattern classification analyses were implemented using the Princeton MVPA toolbox ( and custom MATLAB code. An anatomically defined mask composed of the visually selective areas of the ventral temporal lobe was used for MVPA classification.

A cortical parcellation of the high-resolution T1 SPGR image was obtained for each participant using FreeSurfer (Martinos Center for Biomedical Imaging, MGH, Charlestown, MA) and the resulting left and right inferotemporal cortex, fusiform gyrus, and parahippocampal gyrus were combined to serve ISRIB datasheet as the mask for MVPA classification. The classifier was first trained to differentiate object and scene processing on data from the encoding localizer task; we then validated the classifier’s ability to measure reactivation of unseen, recalled content by applying it to data from the guided recall task (see Figure S1 and Supplemental Experimental Procedures). The main goal of the MVPA approach was to assess whether events that overlap with existing memories lead to the reactivation of unseen, related content. To do so, MVPA classifiers trained on the encoding localizer were applied to the encoding data from associative inference paradigm to provide

a measure of content-specific reactivation during overlapping events. For each participant, a regressor matrix labeled the time series by encoding condition (e.g., first repetition of AB associations for OOO triads, Histone demethylase first repetition of AB associations for OOS triads, etc.; 36 time points per condition). To account for the hemodynamic lag, condition labels were shifted back by three scans (6 s) with respect to the functional time series. The mean classifier output for each content class (object, scene) was then extracted for each experimental condition. As the critical measure of reactivation, we assessed the change in classifier output across repetitions of AB associations (last-first AB presentation) where the presented class of content was the same (e.g., two objects for OOO and OOS triads), but the content class of the third, unseen triad member differed (i.e., object versus scene; Figure 2).

Due to the asymmetry in the boundary conditions, the distal synap

Due to the asymmetry in the boundary conditions, the distal synapse induces a larger hyperpolarization at the hotspot compared to the proximal synapse. Both the larger hyperpolarization and the larger SL at the hotspot generated by the distal synapse are combined to enhance its inhibitory impact on the hotspot (and thus on the soma firing) as compared to the proximal synapse ( Figure 2C and see more detailed analysis in Figures S5–S7). These results are also valid for different

loci with respect to the hotspot of the inhibitory synapses along the dendritic cable model ( Figure S5). Note that the results in Figures 1 and 2 hold for any dendritic region producing High Content Screening inward current (e.g., via an α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid [AMPA]

synapse). But the advantage of distal versus proximal inhibition at that region is amplified in the voltage-dependent (nonlinear) case (e.g., NMDA currents as in Figures 1B and 2B or active Ca+2 or Na+ inward currents) because inhibition at the hotspot this website increases the threshold for the activation of regenerative inward currents (Jadi et al., 2012). We also note that the advantage of the “off-path” inhibition over the corresponding “on-path” inhibition in dampening a local dendritic hotspot is augmented in distal thin dendrites because, in such branches, the asymmetry in (distal versus proximal) boundary conditions is even larger than the cylindrical case modeled in Figures 1 and 2 (Rall

and Rinzel, 1973). Figure 3 depicts SL in the case of an idealized branched dendritic tree ( Rall and Rinzel, 1973) receiving Histone demethylase a single conductance perturbation in a distal dendritic terminal. For comparison, the steady voltage (V, dotted line) attenuation is also shown. V attenuation is steep from the distal (input) branch toward the branch point (P) but is shallow in the direction of the sibling branch S ( Figure 3, black arrow) because of the sealed-end boundary condition in this branch ( Rall and Rinzel, 1973; Golding et al., 2005). Similarly to V, SL attenuates steeply toward the soma; however, in contrast to V, SL attenuates steeply toward terminal S (blue line). This follows directly from Equation 3, as SL attenuation from P to S depends on the (steep) voltage attenuation from S to P (AS,P). Consequently, the impact of conductance perturbation diminishes rapidly with distance in such thin dendritic branches. Hence, excitatory currents in distal dendrites are electrically “protected” from the inhibitory shunt, unless the inhibitory synapses directly target these branches. In the realistic case, the dendritic tree receives multiple inhibitory synapses; even a single inhibitory axon typically contacts the postsynaptic dendritic tree at multiple loci, often making more than ten synapses in the postsynaptic dendritic tree (Markram et al., 2004).

To further address this possibility, we turned to enhancer-supres

To further address this possibility, we turned to enhancer-supressor genetic assays dependent on Sema-1a/PlexA repulsive axon guidance. Ectopic expression of Sema-1a in muscles leads to reduced muscle innervation (Yu et al., 1998). These effects are due to the repulsive action of Sema-1a (Yu et al., 1998) and are suppressed by decreasing the levels

of the Sema-1a receptor, PlexA (Winberg et al., 1998b). In contrast, decreasing Palbociclib solubility dmso the levels of 14-3-3ε enhanced Sema-1a repulsion (Figures 3B–3D); suggesting that similar to PKA RII and Nervy (Figures 3C and 3D; Terman and Kolodkin, 2004), 14-3-3ε opposes Sema-1a repulsion. To further investigate these antagonistic interactions, we turned to genetic assays dependent on the repulsive effects of PlexA. Increasing the levels of neuronal PlexA generates abnormally defasciculated axons that result in discontinuous CNS longitudinal connectives and axons crossing the midline or projecting abnormally into the periphery ( Figures 4A and S3A; Metformin price Winberg et al., 1998b and Ayoob et al.,

2004). Strikingly, decreasing the levels of 14-3-3ε significantly increased these PlexA-dependent guidance defects ( Figures 4A–4C), while increasing neuronal 14-3-3ε significantly decreased these PlexA-dependent guidance defects ( Figures 4B and 4C). Together, these results along with other in vivo Sema1a/PlexA-dependent CNS and motor axon guidance assays ( Figure S3) indicate that 14-3-3ε antagonizes Sema1a/PlexA-mediated repulsive axon guidance. To begin to investigate the mechanism only by which 14-3-3ε antagonizes Sema-1a/PlexA-mediated repulsive axon guidance, we sought to determine the site of interaction between PlexA and 14-3-3ε. We found that the portion of PlexA that was necessary and sufficient for the interaction with 14-3-3ε contains a consensus 14-3-3 binding sequence (Figures 5A and 5B).

In particular, 14-3-3 proteins typically bind to single phosphorylated serine or threonine residues on target proteins (Yaffe and Elia, 2001) and Drosophila PlexA contains a mode I 14-3-3 consensus binding motif, R/KSXpSXP, where p represents the phosphorylated serine (Ser1794) residue predicted to mediate the interaction with 14-3-3 proteins ( Figure 5B; Yaffe et al., 1997 and Rittinger et al., 1999). To test this possibility, we substituted alanine (Ala) for serine (Ser) and threonine (Thr) residues within this consensus 14-3-3ε binding motif. We found that the predicted Ser1794 residue was necessary for the observed PlexA interaction with 14-3-3ε ( Figures 5C and S4A). Next, we generated phospho-mimetic forms of Ser1794 (Ser1794 to Glu1794 or Asp1794), but found that as with other 14-3-3 interacting proteins adding one negative charge was not sufficient for the interaction between PlexA and 14-3-3ε ( Figures 5C and S4A).

g , the epilepsies, as well as other neurological and non-neurolo

g., the epilepsies, as well as other neurological and non-neurological conditions) may be a collection of rare and often private genomic disorders due to mutations in genetically intolerant genes (Petrovski et al., 2013). The International League Against Epilepsy classification of epilepsy includes information about seizure type, age of onset, response to antiepileptic drugs, electroencephalogram (EEG) and structural brain imaging information, and prognostic considerations. From a molecular and physiological perspective, however,

it is clear that this scheme often bears little selleck chemicals relationship with underlying biology. Copy-number variants are associated with a range of epilepsy subtypes (Heinzen et al., 2010), including focal

epilepsy, which responds to surgery (Catarino et al., 2011); causal Cilengitide molecular weight mutations in SCN1A show very complex genotype-phenotype relationships ( Zuberi et al., 2011); and mutations in the gene encoding DEPDC5 are responsible for a significant proportion of cases of familial nonlesional focal epilepsy ( Dibbens et al., 2013). The National Academies has recently recognized the need for “a new taxonomy of human disease based on molecular biology” in its publication Toward Precision Medicine ( National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease, 2011). NGS can facilitate individualized molecular diagnoses in patients and families with hitherto undiagnosed out and unexplained disorders. The traditional diagnostic model in the evaluation of an individual with a putative genetic disorder includes formulation of a diagnostic hypothesis that may include a diverse range of possibilities. These possible diagnoses are then tested by a variety of biochemical (blood, urine, cerebrospinal fluid [CSF]), structural (MRI), functional (EEG), and specific gene analyses. A recent study examined the economic implications of WES-based diagnosis in the context of 500 patients evaluated using traditional genetic tests ( Shashi et al., 2013). This work showed that if the diagnosis is not clinically apparent at the first visit, then the cost on average per

successful genetic diagnosis using traditional tests is approximately $25,000. The cost of WES, on the other hand, is now well under $1,000 per sample. Thus, when used in an appropriate setting, WES has the potential to provide significant cost benefit to the healthcare budget and to society. Diagnostic sequencing should, and probably will, find wide, immediate application in the care of patients with neurological disease. The realization of its full potential will require addressing a number of key bottlenecks. Of particular importance is the challenge of data integration. Clearly, to maximize the benefit of WES-based diagnostics, it is critical to be able to compare the sequences of patients evaluated in different academic medical centers.

, 2001), but deleting ASIC2 or ASIC3 has only subtle effects on t

, 2001), but deleting ASIC2 or ASIC3 has only subtle effects on the activity of mechanoreceptor fibers (Price et al., 2000 and Price et al., 2001). Moreover, neither ASIC2 nor ASIC3 is essential for MeT currents studied in cultured DRG neurons (Drew et al., 2004). Collectively, these investigations indicate that no single ASIC subunit is essential for the function of mechanoreceptor neurons in mice and suggest that the function of such neurons is robust to genetic deletion buy PF-02341066 of channel proteins. In principle, genetic deletion of a shared auxiliary subunit could reveal more severe deficits in behavioral and cellular responses

to touch because such proteins might affect the function of multiple channel-forming subunits. The impact of genetic deletion of SLP3 illustrates the power of this idea (Wetzel et al., 2007). SLP3 (also known as STOML3) is a stomatin-like protein that binds to both ASIC2 and ASIC3 and alters the activity of ASIC channels in heterologous cells. It is orthologous to the C. elegans protein MEC-2, which is required for MeT currents in vivo and enhances MEC-4-dependent currents in heterologous cells ( Goodman

et al., 2002, Huang and Chalfie, 1994 and O’Hagan et al., 2005). Genetic deletion of SLP3 decreases the proportion of mechanically sensitive Aβ and Aδ fibers that innervate the skin and the proportion of dissociated DRG neurons with mechanosensitive currents ( Wetzel et al., 2007). Additionally, loss of SLP3 disrupts texture sensing. These data suggest that many, but not all mechanoreceptors depend on SLP3 and its DEG/ENaC binding partners to detect mechanical stimuli. DAPT mouse Mirroring the effects of single ASIC gene deletions are those of TRP channel gene deletions: loss of a single channel gene has only subtle effects on somatosensory nerve fiber function and no single TRP channel gene deletion leads to a loss of mechanosensitivity in an individual fiber class (Kwan et al., 2006, Kwan et al., 2009, Liedtke and Friedman,

2003 and Suzuki et al., 2003a). But, loss of TRP channel proteins has clear effects on the response of nociceptors to inflammation. Here, we provide three examples. First, noxious chemical agents such as mustard oil potentiate behavioral responses to mechanical stimuli, an effect which is muted in TRPA1 knockout mice (Bautista et al., 2006). Loss 4-Aminobutyrate aminotransferase of TRPA1 also disrupts sensitization produced by injection of bradykinin, a peptide released by damaged tissue (Kwan et al., 2009). Second, genetic deletion of TRPV4 has subtle effects on behavioral and neural responses to mechanical cues (Chen et al., 2007 and Suzuki et al., 2003a) but produces significant deficits in inflammation-induced sensitization. Compounds induced by inflammation (prostaglandin E2 and serotonin) decrease the threshold for mechanical activation of C fiber nociceptors in wild-type, but not in TRPV4−/− ( Alessandri-Haber et al., 2005 and Chen et al., 2007).

In contrast to physiological conditions with 10 mM [Cl−]in (ECl n

In contrast to physiological conditions with 10 mM [Cl−]in (ECl near the resting potential), whole-cell recording with 130 mM [Cl−]in revealed that, while the membrane voltage and input resistance remained unchanged, the spike duration is longer and the threshold for spike generation is lowered (Table 1; Figure S2). R428 order While the former effect is attributable to CaCC, the latter may also involve Cl− channels that are constitutively active in a resting neuron. Whereas under physiological conditions both K+ efflux through K+ channels and Cl− influx through Cl− channels that are constitutively active counter sodium

channel activation during depolarization in setting the threshold for spike generation, elevating internal Cl− leads to Cl− efflux through these Cl− channels Selleck Ku 0059436 thereby enhancing rather than dampening the excitability. During normal development, a neuron switches from higher internal Cl− to lower internal Cl−, shifting ECl and converting Cl− channel activity from excitatory to inhibitory (Ben-Ari, 2002). Whereas most mature neurons normally have low internal Cl− (5–10 mM) to allow Cl− channels to provide inhibition, extended periods of high neuronal activity or pathological conditions such as seizures and brain traumas can lead to accumulation

of internal Cl− and revert Cl− channel activity back to an excitatory conductance as that during development (Blaesse et al., 2009, De Koninck, 2007 and Payne and et al., 2003). The susceptibility of cation-chloride cotransporters to modulation renders the Cl− gradient a dynamic readout of neuronal activity. For example, in mature hippocampal neurons, spike firing can alter the Cl− gradient via activity-dependent phosphorylation of KCC2, a K+-Cl− co-transporter that normally extrudes Cl−, resulting in Cl− accumulation and a positive shift in ECl (Fiumelli et al., 2005 and Woodin et al., 2003). This activity-dependent shift in the Cl− gradient will cause CaCCs to progressively lose their grip over the action potential

duration and threshold for spike initiation by synaptic potentials, as well as EPSP amplitude and summation. Epilepsy patients exhibit upregulation of NKCC1 (Cl− accumulator) and downregulation of KCC2 (Cl− extruder) in the temporal lobe, resulting in a positive shift in ECl (Palma et al., 2006). In hippocampal slices, KCC2 undergoes downregulation after sustained interictal-like activity in zero-Mg2+ conditions (Rivera et al., 2004). Similar positive shift in ECl due to altered Cl− gradient also takes place with brain trauma (Bonislawski et al., 2007) and axonal injury (Nabekura et al., 2002). Thus, impairments of Cl− homeostasis would turn CaCC modulation into positive feedback to further exacerbate the excitotoxicity.

This event was observed in all treatments evaluated In the micro

This event was observed in all treatments evaluated. In the microbiological control evaluated (mortadella without target microorganism), C. perfringens counts were not detected during all the storage time, showing non-interference in the observed results. The extraction yield value of S. montana EO was similar to that found by Ćavar et al. (2008). However the yield found Verteporfin mouse in our study was lower than the yield reported by the following groups: Bezbradica et al., 2005, Mastelić and Jerković, 2003 and Radonic and Milos, 2003. The phytochemical profile found for the winter savory EO in this study

was in agreement with the results observed by several authors who have also evaluated this vegetal specie ( Radonic and Milos, 2003, Skočibušić and Bezić, 2003, Mastelić and Jerković, 2003 and Silva et al., 2009). In contrast, the savory EO evaluated by Ćavar et al. (2008) was characterized by a high content of alcohols, such as geraniol and terpinen-4-ol. The final composition of EO is genetically influenced with specificity to the following factors: each organ and its stage of development; climatic conditions of the plant collection site; degree of terrain hydration; level of macronutrients and micronutrients;

and drying conditions to which the plant material is exposed to ( Burt, 2004 and Bakkali et al., 2008). Slavkovska et al., 2001 and Mirjana and Nada, 2004 reported the chemical HDAC phosphorylation variability of S. montana EO according to factors like plant stage of development and different geographic locations. The antimicrobial properties of winter savory EO are related to the presence of its major chemical compounds, such as thymol and cravacrol in the EO fraction ( Mirjana and Nada, 2004 and Radonic and Milos, 2003). The formation of growth inhibition

zones on the tested growth bacterial cultures showed the antimicrobial effect of S. montana EO. The MIC is cited by most researchers as the measure of performance of antibacterial EOs ( Burt, aminophylline 2004). Considering the large number of different groups of chemical compounds present in EOs, it is likely that their antibacterial activity is not attributable to one specific mechanism but to several targets in the cell. An important characteristic of EOs is their hydrophobicity, which allows the accumulation and partition of the lipids in bacterial cell membranes modifying their structure, distorting the lipid/protein interactions and disturbing their function ( Juven et al., 1994, Sikkema et al., 1994 and Sikkema et al., 1995). The loss of differential permeability of the cytoplasmatic membrane is considered the cause of cell death.

When the sample was stratified by clinical status, rs769449 showe

When the sample was stratified by clinical status, rs769449 showed a strong and similar effect size in both cases (n = 519; Beta: 0.067; p = 3.38 × 10−6) and in controls (n = 687; Beta: 0.075, p = 1.54 × 106) with CSF

ptau levels ( Table S2). Several studies have suggested that up to 30% Hydroxychloroquine manufacturer of elderly nondemented control samples meet neuropathological criteria for AD ( Price and Morris, 1999; Schneider et al., 2009). It has also been shown that individuals with CSF Aβ42 levels less than 500 pg/ml in the Knight-ADRC-CSF, and 192 pg/ml in the ADNI series have evidence of Aβ deposition in the brain, as detected by PET-PIB ( Fagan et al., 2006; Jagust et al., 2009). Individuals with CSF Aβ42 levels below these thresholds could be classified as preclinical AD cases with the presumption that some evidence of fibrillar Aβ deposits would be detected ( Fagan et al., 2006; Jagust et al., 2009). When we used these thresholds, rs769449 showed a significant association with CSF tau and ptau in both strata, although the effect size was almost two-fold higher in individuals with high Aβ42 levels (n = 416; Beta: 0.072; p = 6.58 × 10−5, for CSF tau levels) than in individuals with low Aβ42 levels (n = 478; Beta: 0.035; p = 1.83 × 10−2, for CSF tau levels; Table S2). These results indicate that the residual association of SNPs in the SB203580 molecular weight APOE region is

not dependent on clinical status or the presence of fibrillar Aβ pathology and clearly suggests that DNA variants in the APOE gene region influence tau pathology independently of Aβ or AD disease status. To analyze whether there is more than one independent signal in the APOE gene region, APOE genotype was included in the model as a covariate ( Table 4; additional figures on The association for the SNPs located in the APOE region was reduced drastically

(p values between 0.02 and 0.008), suggesting that most of the association in this locus is driven by APOE genotype. Outside the APOE region, we detected genome-wide significant association with three loci for CSF tau, ptau, or both at 3q28, 9p24.2, and 6p21.1. Dichloromethane dehalogenase Several SNPs in each locus showed highly significant p values ( Figure 1). For all loci, at least one SNP was directly genotyped ( Table 2) and each of the data sets contributed to the signal, showing similar effect sizes and direction ( Table S3), suggesting that these are real signals and unlikely to be the result of type I error. The strongest association for CSF tau, after APOE, is rs9877502 (p = 4.98 × 10−09), located on 3q28 between GEMC1 and OSTN and the noncoding RNA SNAR-I ( Figures 1 and 2). Fifty-five intragenic SNPs located between SNAR-I and OSTN, showed a p value lower than 9.00 × 10−05 (additional information on