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% selleck chemicals 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 Natural Product Library datasheet 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 https://hopecenter.wustl.edu/data/Cruchaga_Neuron_2013). 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. Thalidomide 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 https://hopecenter.wustl.edu/data/Cruchaga_Neuron_2013).

In boxing, about ten deaths have occurred

annually during the 20th century; most were related to knockout or technical knockout (deaths due to boxing are registered in the Manuel Velazquez Boxing Fatality Collection, available at http://ejmas.com/jcs/jcsart_svinth_b_0700.htm). The most common cause http://www.selleckchem.com/products/sch772984.html of death is subdural hematoma (Guterman and Smith, 1987; Unterharnscheidt, 1995). Most deaths (about 80%) are among professional boxers, and boxing-related deaths due to brain damage occur in all rounds and in all weight classes, but somewhat surprisingly, most deaths are in lower weight classes. Deaths declined since 1983, which might be related to lower exposure to repetitive head trauma among professional boxers with shorter careers and fewer fights (Baird et al., 2010). Catastrophic brain injury also occurs

in American Selleck PFI-2 football. During the second half of the 20th century, more than 400 players died from brain or spinal cord injury in the United States while playing (McIntosh and McCrory, 2005). Repetitive brain trauma may cause chronic neurological problems. For example, in 1928, Martland (1928) described chronic brain damage in boxers, which was termed punch drunk syndrome. A few years later, Millspaugh (1937) called this syndrome dementia pugilistica, which is more commonly used. Forty years ago, Corsellis et al. (1973) described neuropathological changes in a series of professional boxers with dementia pugilistica. Their key findings included neurofibrillary tangles in cortical areas, cerebellar atrophy and gliosis, hypopigmentation of the substantia nigra, and cavum septum pellucidum. Many years after these early studies documenting the histopathological

Thymidine kinase changes in career boxers, it became evident that a similar chronic brain condition occurred in athletes who practiced other contact sports and had a history of repeated head trauma, and it was only recently that the first autopsy report from a football player was published (Omalu et al., 2005). Neuropathological changes were similar to those in boxers with dementia pugilistica, findings that have now been verified in larger studies (McKee et al., 2009). These authors introduced the more general term chronic traumatic encephalopathy (CTE), which has gained broader usage (Stern et al., 2011). CTE is regarded as a chronic brain syndrome due to effects of repetitive brain trauma, but there are no generally accepted guidelines for a clinical diagnosis of CTE or for how to distinguish neuropathological changes due to CTE from those due to aging and Alzheimer’s disease (AD). CTE is regarded as a neurodegenerative disorder that often occurs in midlife, years or decades after the sports career has ended (McKee et al., 2009). About one-third of CTE cases are progressive (Roberts, 1969), but clinical progression is not sequential or predictable.

The flow cytometry data were analyzed and scatter profiles for fluorescence intensities plotted using Flowjo software (Treestar, Ashland, OR, version 8.8.6). Three-week-old rat neuron cultures expressing GFP-htau (WT, P301L, AP, AP/P301L, E14, E14/P301L) were lysed (50 mM Tris-HCl, 150 mM Lenvatinib datasheet NaCl, 1 mM EDTA, 1.5% Triton X-100, 0.1% Na deoxycholate, phosphatase inhibitors [phenylmethylsulfonyl fluoride, phenenthroline monohydrate, and

phosphatase inhibitor cocktails I and II; 1:1000, Sigma] and protease inhibitor cocktail [1:100; Sigma]; 30 min at 4°C on shaker), scraped and lysates were collected for determination of total protein concentration by the BCA protein assay. An aliquot of each sample (450 μg) was diluted in 1 ml of dilution buffer (50 mM Tris-HCl, 150 mM NaCl [pH 7.4] and freshly added protease and phosphatase inhibitors) selleck chemicals llc and immunodepleted with 150 μl protein A and 150 μl protein G for 1 hr at 4°C. Immunodepleted samples were incubated with 30 μg Tau-13 antibody and 100 μl protein G Sepharose beads overnight at 4°C. The next day, beads were

washed with buffer A (50 mM Tris-HCl, 0.1% Triton X-100, 300 mM NaCl, 1 mM EDTA) for 20 min at 4°C followed by a wash with buffer B (50 mM Tris-HCl, 0.1% Triton X-100, 150 mM NaCl, 1 mM EDTA) for 20 min at 4°C. Sample was eluted off the beads using 1X sodium dodecyl sulfate (SDS) loading buffer, heated to 95°C for 10 min and analyzed by western blot analysis as described using the Tau-13 (total tau), pS199, pT231, and Alz-50

antibodies. Preparation of postsynaptic densities from mouse brains was performed based on the procedures of Cheng et al. (2006). Briefly, PSD fractions were prepared from mouse forebrains at 4°C. Forebrains were collected from adult mice and homogenized in ice-cold Buffer A (6 mM Tris [pH 8.0], 0.32 M sucrose, 1 mM MgCl2, 0.5 mM CaCl2, phosphatase inhibitors Montelukast Sodium [phenylmethylsulfonyl fluoride, phenenthroline monohydrate, and phosphatase inhibitor cocktails I and II; 1:100, Sigma] and protease inhibitor cocktail [1:100; Sigma]). The resulting extract was centrifuged at low speed (1400 × g for 10 min) to collect the first supernatant (S1). The pellet (P1) was re-extracted and homogenized with buffer A and centrifuged at 710 × g for 10 min. This supernatant (S1′) was pooled with the S1 supernatant and then centrifuged at 710 × g for 10 min. Then, the supernatant was removed and centrifuged at 13,800 × g for 12 min to isolate the S2 supernatant used for western blotting. The S2 fraction contains both pre- and postsynaptic proteins. The pellet (P2) was resuspended and homogenized in Buffer B (0.32 M sucrose and 6 mM Tris [pH 8.0] with the same phosphatase and protease inhibitors). This homogenate was loaded onto a discontinuous sucrose gradient (0.85/1/1.15 M in 6 mM Tris [pH 8.0]), and centrifuged at 82,500 × g for 2 hr. The synaptosome fraction (Syn) between 1 M and 1.

“
“Not long after John O’Keefe and Jonathan Dostrovsky discovered place cells (O’Keefe and Dostrovsky, 1971), hippocampal neurons that preferentially fire action potentials when an animal is located in specific parts of an environment, Gary Lynch complained to John O’Keefe, “I’ve tested your theory about these place cells and the spatial function of the hippocampus. I put my slice on wheels, moved it around the lab and it made no difference at all” (Seifert, 1983). Although disconnected from natural behaviors, slice Ceritinib chemical structure preparations have remained

the primary method of studying the intracellular dynamics of hippocampal cells until recently because of the daunting challenge of keeping a micropipette stable in a moving animal. In this issue of Neuron, a study by Epsztein et al. (2011) is part of an emerging body of literature that uses recently developed methods for intracellular recording of neurons in awake, behaving animals, adding rich details of subthreshold membrane potential dynamics to previous selleck inhibitor findings from extracellular recording studies. Obtaining an intracellular recording in an awake, behaving animal is extremely difficult and requires addressing the issue of mechanical stability. In recent years,

two different methods have been developed to solve the stability problem. In the first method, which was used by Epsztein et al. (2011), hippocampal neurons are patched while the rat is under anesthesia, and the electrode is rigidly attached to the skull for stability (Lee et al., 2009). Then the anesthesia is rapidly reversed with an injection of an antagonist so the rat can wake up and explore an environment while the intracellular recoding continues for about another 10 min. In the second method, a mouse’s skull is attached to a rigid head plate while a neuron is patched (Harvey et al., 2009). While holding the head plate in place, the mouse is allowed to run on a spherical treadmill (essentially, a large floating ball) in front of a video screen displaying a virtual maze. Thus, the head-fixed mouse can run and navigate a virtual environment during the intracellular recording. Both methods have been used to record from hippocampal Parvulin place

cells and have found depolarization peaks surrounding action potentials that fired within place fields. Methods of intracellular recording in awake, behaving animals can be applied to a range of different investigations but are particularly useful for studying neurons that are difficult to record by using traditional techniques, such as silent cells. Silent cells are hippocampal pyramidal cells that fire few or no spikes in an environment. In any given environment, approximately 40% of hippocampal pyramidal cells are place cells, and the remaining 60% are silent cells (Thompson and Best, 1989). Although silent cells were identified by using extracellular recordings, they are challenging to study extracellularly because of their low (or zero) firing rate during a given task.

In this section, we emphasize the research studies that support this conclusion and then consider how sensory

and nonsensory factors might account for the findings. Frequency resolution (tone detection in the presence of a second nearby tone) matures first for low frequencies, but is adult-like by 6 months at all frequencies tested (Spetner and Olsho, 1990, Schneider et al., 1990 and Hall and Grose, 1991). This corresponds to cochlear development, including functional measures suggesting that the low frequency region of the cochlea matures somewhat earlier (reviews: Rübsamen and Lippe, 1998 and Abdala and Keefe, 2012). In contrast, frequency discrimination (i.e., hearing a difference between two tones presented sequentially) does not mature until roughly 10 years of age for low-frequency tones (Maxon and Hochberg, selleck chemicals 1982, Olsho, 1984, Sinnott and Aslin, 1985, Olsho et al., 1987, Jensen and Neff, 1993, Thompson et al., 1999 and Moore et al., 2011). To detect a difference in intensity between two sounds, infants require about a 6 dB increase; this declines to 2 dB by 4 years of age for sounds of sufficient

duration, but may not be fully mature until 10 years (Sinnott and Aslin, 1985 and Maxon and Hochberg, 1982). Thus, even for the most basic auditory percepts, human performance emerges gradually over nearly a decade. Temporal processing displays a range of developmental time courses. Selleck PD-1/PD-L1 inhibitor For example, juveniles (those who have passed infancy, and have adult-like Axenfeld syndrome cochlear processing, but who have not yet reached sexual maturity) and adults display differences in temporal integration, the process whereby information is summed over time, resulting in the best possible detection or discrimination thresholds (Maxon and Hochberg, 1982, Berg and Boswell, 1995 and Moore et al., 2011). Figure 2

shows two experiments in which tone threshold was determined at both a short and a long duration. In both cases, the young subjects display greater improvement (blue Δ) than adults (red Δ). This is because their performance is exceptionally poor at the short stimulus durations. The ability to discriminate duration differences matures later, dropping from 80 to 20 ms between 6 years and adulthood (Elfenbein et al., 1993 and Jensen and Neff, 1993). Some temporal processing skills such as the detection of amplitude and frequency modulations (AM and FM) are exceptionally slow to mature. These cues are a predominant component of communication sounds, including speech (Rosen, 1992, Shannon et al., 1995 and Singh and Theunissen, 2003). In humans, the detection threshold for AM stimuli continues to mature beyond 12 years (Banai et al., 2011).

Log10 transformed frequency of each word was used to scale the error derivatives. This level of frequency compression was employed to reduce the training time in this large model (Plaut et al., 1996). The error (difference between the target and the output patterns) was estimated with the cross-entropy method (Hinton, 1989). No error was backpropagated from a unit if the difference between

the output and the target was <0.1 (i.e., zero-error radius parameter was set to 0.1). Momentum was not used in this study. All the units in the hidden and output layers had a trainable bias link, except for the copy and Elman layers. Weights were initialized to random values between −1 and 1 (0.5 for recurrent connections). Weights Ipilimumab datasheet from the bias unit to hidden units were initialized at −1, so as to avoid strong hidden unit activation early in training (Botvinick and Plaut, 2006). A sigmoid activation function was used for each unit with activation ranging from 0 to 1. At the beginning of each trial, activations for all units in the hidden layer (including vATL-output layer) were set to 0.5, and for all units in the insular-motor output layer to zero. This work was supported Palbociclib by an

MRC programme grant to M.A.L.R. (G0501632), a Royal Society travel grant to M.A.L.R. and S.S., and a Study Visit grant from the Experimental Psychology Society to T.U. T.U. was supported by an Overseas Research Scholarship (UK) and an Overseas Study Fellowship from the Nakajima Foundation (Japan). “
“Language processing depends not only on cortical regions, but also on the

white matter fiber bundles that connect them (Geschwind, Teicoplanin 1965, Wernicke, 1874 and Friederici, 2009). Traditionally the arcuate fasciculus was considered to be the main pathway connecting frontal and temporal language areas (Geschwind, 1965). However, recent studies using diffusion tensor imaging (DTI) have revealed that frontal and temporal language regions are connected by multiple dorsal and ventral tracts. Dorsal tracts include not just the arcuate fasciculus, but also other branches of the superior longitudinal fasciculus (SLF) (Catani et al., 2005, Frey et al., 2008, Glasser and Rilling, 2008, Makris et al., 2005 and Makris and Pandya, 2009). Ventral tracts include the extreme capsule fiber system (ECFS), which connects the frontal operculum to mid-posterior temporal cortex, and the uncinate fasciculus (UF), which connects the orbitofrontal region to anterior temporal cortex (Anwander et al., 2007, Croxson et al., 2005, Frey et al., 2008, Friederici et al., 2006, Makris and Pandya, 2009, Parker et al., 2005 and Saur et al., 2008). Syntax is one important component of language and has been shown in functional imaging studies to depend on both frontal and temporal language regions (Bornkessel et al., 2005, Wilson et al., 2010a and Pallier et al., 2011).

Multiple CB splice variants exist that differ in their C-terminal structures and by the presence or absence of an N-terminal SH3 domain (Kins et al., 2000 and Harvey et al., 2004). Intriguingly, the predominant CB isoforms detected in vivo contain an SH3 domain, which inhibits the aforementioned CB-dependent formation of submembrane gephyrin clusters, indicating that CB is negatively regulated by its SH3 domain (Kins et al., 2000 and Harvey et al., 2004). However, cotransfection of CBSH3+ and gephyrin with NL2 negates the inhibitory effect SCR7 of the SH3 domain (Poulopoulos

et al., 2009). CB splice variants invariably contain a pleckstrin homology (PH) domain that is required for its interaction with plasma-membrane-restricted

phosphoinositides and for clustering of gephyrin at inhibitory synapses (Harvey et al., 2004 and Reddy-Alla et al., 2010). selleck chemical The data are consistent with a heterotrimeric membrane-associated complex that consists of NL2, CBSH3+, and gephyrin and that enables the selective deposition of gephyrin at NL2-containing inhibitory synapses. Experiments in heterologous cells indicate that NL1 can potentially substitute for NL2 and similarly induce submembrane gephyrin clusters but only with constitutively active CB isoforms that lack an SH3 domain. In addition, preliminary evidence suggests that the α2 subunit can substitute for NL2 and activate the gephyrin-clustering function of CBSH3+ (Saiepour et al., 2010). This GABAAR-dependent function of CB is specific for α2-containing receptors and abolished by a naturally occurring missense mutation (CBG55A) that disrupts the clustering of α2-containing GABAARs and gephyrin in cultured neurons and is associated with mental retardation, epilepsy, and hyperekplexia in a patient (Harvey et al., 2004 and Saiepour et al., 2010). NL1- and α2 subunit-mediated activation of CB might contribute to residual clustering of gephyrin seen in NL2 KO mice (Jedlicka et al., 2011). Other gephyrin binding proteins cAMP that are concentrated at synapses include the Ser/Thr kinase mTor (mammalian target of rapamycin,

also known as RAFT1 and FRAP1) (Sabatini et al., 1999) and the dynein light chains (DLC) 1 and 2 (Fuhrmann et al., 2002). mTor functions as an important regulator of mRNA translation, allowing for speculation that gephyrin might contribute to translational control of postsynaptic protein synthesis. This idea is supported by recent evidence that both gephyrin and collybistin are part of the eukaryotic translation initiation factor 3 complex (Sertie et al., 2010). However, whether gephyrin and collybistin play a role in translational control in dendrites remains to be elucidated. The interaction between gephyrin and the DLC is implicated in retrograde vesicular transport of gephyrin-glycine receptor complexes from glycinergic synapses (Maas et al., 2009).

Similarly, exosomes expressing TGFβ derived from the malignant effusion of cancer patients

were reported to promote the increase in number and functionality of Treg in vitro [62]. Another evidence has been reported by Clayton et al., who showed that Autophagy inhibitor exosomes isolated from different tumor cell lines carry surface TGFβ and inhibit T cell proliferation by skewing IL-2 responsiveness in favor of Treg and away from cytotoxic cells [63]. It is worth mentioning that TGFβ-expressing exosomes can also be involved in physiological immune homeostasis. In fact, a recent study indicates that TGFβ expressed in thymic exosomes is required for the generation of Foxp3+ Treg in peripheral tissues, such as lung and liver, and participate in the maintenance of physiological immune

tolerance [64]. The role of tumor exosomes in promoting the expansion of immunoregulatory cell components are beginning to be investigated also in in vivo murine models, representing a crucial step for proving a true involvement of this pathway in immunosuppression and tumor progression. In this regard it should be pointed out that one major hurdle of this type of studies has been so far to assess pharmacokinetics of the injected exosomes that, due to their small dimension, might behave differently compared to whole cells. Technical advances of the PLX3397 last years have enabled the investigating groups not only to trace exosomes after in vivo administration PD184352 (CI-1040) but also to analyze the interaction pathways with host cells, an issue that is still poorly investigated. Most of the experimental evidences on the immunosuppressive role of tumor exosomes point to a potential involvement in the expansion of MDSC, while less information about

the impact of these organelles on Treg, once injected in vivo, are presently available. Immune suppressive pathways generated by adoptively transferred tumor exosomes have been observed in the TS/A mammary tumor murine model, where injected nanovesicles were found to interact with CD11b+ myeloid precursors in the bone marrow (BM) and to block BMDC differentiation by inducing IL-6 production and Stat3 phosphorylation [65]. Similarly, in a breast carcinoma model, tumor-derived exosomes were demonstrated to skew BMDC differentiation toward an MDSC phenotype promoting tumor progression, through a prostaglandin E2 and TGFβ-mediated pathway [66]. Recent data also demonstrated a pivotal role for MyD88 in tumor exosome-mediated expansion of MDSCs and promotion of lung metastasis in C57BL/6j (B6) mice [67]. Likewise, Chalmin et al.

, 2008). All ROIs are modeled as 10 mm diameter spheres centered upon ROI coordinates. For the voxelwise and modified voxelwise networks, all voxels (n = 40,100) within the AAL atlas (Tzourio-Mazoyer et al., 2002) were used as in Power et al. (2011). All voxels are cubes with sides of 3 mm. The subject-specific temporal masks formed from Motion Scrubbing were applied

to each subject’s reprocessed data, and a correlation matrix was calculated from node RSFC time courses (e.g., 264 nodes yields a 264 × 264 correlation matrix in each subject). For the main analyses, 120 subject average matrices were used. All averages and comparisons of correlations use Fisher z(r) transformations for calculations, followed see more by reconversion to Pearson r values for reporting. find more In Figure 2, for consistency with the previous literature, all correlations were used regardless of the distances between nodes. Short-distance correlations can arise from shared patterns of local neuronal activity, but they can also arise from data processing (e.g., blurring, reslicing) and from head motion (Power et al., 2012). To minimize the effects of questionable correlations on network structure, as in Power et al. (2011), short-distance correlations

(Euclidean distance <20 mm) were excluded from graph analyses in Figure 6, Figure 7, and Figure 8. Graphs were formed using the nodes and edges described above. Traditionally, analyses of weighted graphs must ignore

medroxyprogesterone negative edges and explore a range of thresholds to characterize the properties of a network (Rubinov and Sporns, 2010). Proposals have been made to modify some graph theoretic measures for unthresholded matrices (Rubinov and Sporns, 2011), but here we follow the traditional approach. Many real-world networks have edge densities of a few percent or less (see Figure 3), and the graph measures used in this paper are developed in such networks. Accordingly, we applied thresholds to graphs to bring them to similar levels of sparseness (∼10%–2% for the areal graph, 5%–0.5% for the voxel-based graphs) as in Power et al. (2011). In general, results are presented over a range of thresholds to give the reader a sense of the (lack of) dependence of a property upon thresholds, and no formal definition of threshold ranges is proposed since it is essentially arbitrary. Our thresholds matched the ranges used in Power et al. (2011), which were chosen to (1) yield complex and interesting community structures (more than four communities), and (2) occupy a range of edge densities often seen in the real-world networks in which techniques like Infomap and measures like participation coefficients were originally developed.

e., 89 interface sequences from 39 species) to identify pairs of interface segments with the following properties: (1) they share KU-57788 concentration the same symmetry center (position 111), (2) each contains amino acids of opposite charge

at interface residues flanking the symmetry center (i.e., positions 109 and 112), and (3) the charges at positions 109 and 112 in one interface are the opposite of those found at the other interface (Figures 1B and 1C). By swapping parts of interfaces with these properties, we reasoned that we could create chimeric interface segments that would disrupt self-pairing, while simultaneously directing pairing to a complementary yet different interface chimera. One example of such an interface chimera is shown in Figure 1B. A Drosophila Ig2 and silkworm Ig2 interface share an asparagine at position 111, the Drosophila sequence has an aspartic acid at position 109 and a lysine at 112, and the silkworm sequence has an arginine at position 109 and an aspartic acid at 112. Two unique half-interface GDC-0449 concentration segments were then created by flanking the shared symmetry center with amino acids 108–110 and 112–114 from the Drosophila and silkworm sequences, respectively. We predicted

that the resulting chimeras would not support self-binding due to charge incompatibility ( Figure 1B) but that the two chimeras would

bind to each other, because the contacts on each half-interface were seen in a wild-type interface. Two pairs of complementary chimeric interface segments (indicated Ig2.3C/Ig2.4C and Ig2.10C/Ig2.11C) were introduced through mutagenesis of Drosophila Ig2 domains with the most similar interfaces ( Figures 1B and 1C; also see sequence alignment in Figure 1D). To test the binding specificity of each altered variable domain, we inserted complementary else pairs of Ig2 interfaces into ectodomains comprising the same Ig3 and Ig7 domains to generate pairs of closely related chimeric isoforms. We first assessed interactions by using the ELISA-based binding assay in which Dscam1 protein ectodomains were clustered in cis in a limited fashion (presumably tetramers) ( Wojtowicz et al., 2007). The binding of two ectodomains each comprising the N-terminal ten domains was tested as previously described ( Wojtowicz et al., 2007). Wild-type isoforms exhibited strong homophilic interaction, but homophilic binding of each chimera was reduced to background levels ( Figure 1D). Importantly, heterophilic binding of each chimera pair was observed at a similar level to that observed with homophilic binding of the control wild-type isoforms. To gain a more quantitative measure of binding specificity, we performed analytical ultracentrifugation (AUC).