Here, we show that rare de novo copy-number mutations are significantly enriched in bipolar disorder and in schizophrenia. Our study sample included 788 subject-mother-father trios with confirmed parentage. DNA from all subjects was derived from whole blood. Details of the subjects are described

in the Supplemental Experimental Procedures (available online). Diagnoses of subjects included bipolar disorder (n = 185, including 107 with an age at onset ≤ 18), schizophrenia (n = 177), and healthy controls (n = Selleckchem MLN0128 426). While the primary disease focus of this study was BD, the inclusion of an additional schizophrenia cohort served first to replicate the one previous study of de novo CNVs in SCZ (Xu et al., 2008) and second to enable a valid comparison of patterns of de novo CNVs in BD with another disorder. In addition, a small set of autism spectrum disorder (ASD) trios (n = 45), all of which had been included in a previous study (Sebat et al.,

2007) and three of which carried known de novo CNVs, were included as a “positive control” to confirm the sensitivity of our methods for detecting de novo events. We performed high-resolution genome-wide copy-number scans, using the NimbleGen HD2 array comparative genomic hybridization (CGH) platform, on all subjects and their biological parents. Data processing and CNV detection were performed buy Dabrafenib as described in Experimental Procedures. CNV call sets were filtered based on probe ratio (≤0.8 and ≥ 1.2), number of probes (≥10), frequency (<1%), and confidence score (Supplemental Experimental Procedures, Tables S1 and S2, and Figures S1 and S2). Rare CNVs that were present in subjects and not in their parents were subsequently validated and fine mapped using a custom tiling-resolution CGH array (Oxford Gene Technology) (Table S3. Custom Tiling arrayCGH Validation of Putative De Novo CNVs and Document

S1. Figures S1–S3; Tables S1, S2, S4–S8, and S10; and Supplemental Experimental Procedures). Results for the genome-wide scans, tiling array validations, and breakpoint sequencing are illustrated by four examples: a deletion involving much CMIP and PLCG2 genes ( Figure 1I) and an exonic deletion of LINGO2 gene ( Figure 1II) detected in subjects with a diagnosis of BD, and an intronic deletion of CSMD3 gene ( Figure 2I) and a deletion adjacent to UGT8 gene ( Figure 2II) detected in subjects with a diagnosis of SCZ. A total of 23 de novo CNVs were detected and validated in our study sample, including fourteen deletions and nine duplications (Table 1). De novo CNVs ranged in size from 15.1 to 7,178 kb, with a median size of 112 kb, and contained a median of two genes. About one-third (8/23) of de novo CNVs in our study were flanked by segmental duplications (SDs) at one (6/23) or at both boundaries (2/23).

, 2002). Among different γ2-containing GABAARs the α5βγ2 receptors are unique in that they are localized mostly extrasynaptically, as mentioned earlier. NSC 683864 concentration Interestingly, even extrasynaptic α5βγ2 receptors are clustered at the plasma membrane (Christie

and de Blas, 2002) (Figure 5B). Loebrich et al. (2006) have identified radixin as a α5 subunit-interacting protein that is essential for extrasynaptic clustering of α5βγ2 receptors. Radixin is a member of the ERM (ezrin, radixin, moesin) family of proteins, which are known to link transmembrane proteins to the actin cytoskeleton. Transfection of neurons with a dominant-negative radixin construct abolishes the clustering of α5-containing receptors but does not affect GABAAR surface expression nor GABAergic tonic and phasic currents (Loebrich et al., SRT1720 2006). The data suggest that radixin-independent

mechanisms prevent α5-containing receptors from accumulation at synapses. The functional relevance of α5βγ2 receptor clustering in the extrasynaptic membrane is not known. Postsynaptic GABAAR clusters represent diffusional confinement areas containing laterally mobile GABAARs stabilized by gephyrin. Fluorescence recovery after photobleaching (FRAP) was used to compare the mobility of fluorescently tagged GABAARs at postsynaptic and extrasynaptic plasma membrane sites (Jacob et al., 2005). These experiments revealed significantly greater fluorescence recovery rates at extrasynaptic than postsynaptic membrane domains, thereby indicating greater mobility of extrasynaptic than postsynaptic GABAARs (Figure 5B).

Moreover, the fluorescence recovery rate at the periphery of the photobleached area was greater than that at the center, consistent with replenishment of GABAARs from within the plane of the plasma membrane, rather than by insertion into the plasma membrane from intracellular receptor pools. To assess the role of gephyrin in modulating lateral diffusion, FRAP experiments were combined with RNAi knockdown of gephyrin, a treatment that effectively reduced the aminophylline expression of gephyrin but did not affect the accumulation of GABAARs at the plasma membrane. Interestingly, postsynaptic GABAARs of gephyrin-RNAi-treated neurons showed significantly greater FRAP recovery rates than control neurons, indicating that the mobility of GABAARs at postsynaptic sites is restrained by direct or indirect interactions with gephyrin (Jacob et al., 2005). An independent study relied on an ingenious method to mutate and functionally tag GABAARs such that they are permanently inactivated by an inhibitor after receptor activation by GABA (Thomas et al., 2005).

Most of the plant

derived strains fermented D-mannitol, amygdaline, potassium gluconate, l-arabinose, d-xylose, sucrose and gentibiose, but none of the dairy control strains were able to utilize any of these carbohydrates (data not shown) A recent genomic analysis on plant derived Lactococcus confirmed the presence of gene clusters that code for the degradation check details of complex plant polymers such as arabinan, xylan, glucans and fructans and for the uptake and conversion of the plant cell wall degradation products, such as α-galactosides β-glucosides, arabinose, xylose, galacturonate, glucuronate and gluconate as plant derived energy sources ( Siezen et al., 2010 and Siezen et al., 2011). All subsp. lactis isolates were able to coagulate milk (10% RSM) although at different rates (18 to 48 h). The two subsp. cremoris strains (M23-10 and M16-10) failed to coagulate milk even after extended incubation of 7 days. However, these cremoris Selleck Cyclopamine strains fermented milk in less than 18 h when the culture was supplemented with 0.5% glucose

( Table 2). The results show that the two cremoris strains have a functioning proteolytic system but compromised lactose utilization ability. A similar result has been reported recently by Gutiérrez-Méndez et al. (2010) where plant L. lactis isolates showed the slowest growth rates and yield when lactose was used as energy source, compared to those obtained when glucose was used as carbohydrate source. In Terminal deoxynucleotidyl transferase ten of the plant lactococci isolates no plasmid was detected. One plasmid each was detected in two of the subsp. lactis isolates (P-21 and C-3) with size ranging between 80 and 90 kb (data not shown). Previous studies have clearly established

that the majority of strains of dairy L. lactis depend on plasmids for lactose utilization, casein degradation, citrate utilization, bacteriocin production, bacteriophage resistance and slime formation ( LeBlanc et al., 1980, McKay, 1983, Teuber, 1995 and Mills et al., 2006). Based on the PFGE DNA finger-printing data, the isolates could be identified as 10 distinct strains (Fig. 1). Two grass isolates 144-L and 144-S showed identical restriction pattern and thus assumed to be the same strain. The other two fresh green pea isolates, P-5 and P-8 also showed identical restriction patterns and growth characteristics and were assumed to be the same strain (Fig. 1). The formation of volatile flavour compounds in dairy products is a complex process resulting from glycolysis, lipolysis and proteolysis of milk components which is mediated by the enzymatic mechanisms of the microflora contained within the product (McSweeney and Sousa, 2000, Smit et al., 2004). However, the production of flavour compounds in fermented dairy products is strain dependent and therefore the composition of the starter mix can greatly influence the flavour profile (Kieronczyk et al., 2003).

SDC was calculated from the models’ softmax likelihood by equalizing selleck compound Pc/a for choosing and avoiding using the following equation: SDC = abs(Pc/a − 0.5) × 2. The result ranged from 0 (maximal insecurity) to 1 (absolute preference of one option). Feedback-locked data were analyzed separately for the categorical conditions fictive and real. Predictors included

the PE (δt), variable learning rate (αt), and a dichotomous regressor indicating a switch of response (coded as 1) or a stay (coded as 0) on the next trial that the same stimulus was shown again. Standardized b values can be assumed to be Gaussian due to the central limit theorem and thus could be tested via two-tailed CT99021 purchase one-sample t tests, which were done separately at each data point in a whole-brain approach across subjects. Resulting p values were corrected for multiple comparisons using false discovery rate (FDR) following the method suggested by Benjamini and Yekutieli (2001), which has been shown to provide solid control of the family-wise error rate (FWER) in EEG data ( Groppe et al., 2011). However, as FDR in itself does not provide strong (local) control of the FWER, it was applied to all concatenated b value data sets per model. This ensured that

all corrections were done with the same threshold value for each regressor in the models. H0 was rejected for all p < 0.00070 in the feedback Farnesyltransferase and p < 0.00045 in the stimulus-locked model. Nonsignificant data points are masked in white in the topography plots and Movie S1. Both conditions in the feedback-locked epochs were contrasted via paired two-tailed t tests thresholded at the same level as noted above. We compared both real and fictive feedback processing directly via paired two-sided t tests of the regression b values,

thresholded at the same level determined by FDR. This revealed that feedback processing indeed differed significantly for all PE effects. The late parietal effect did not differ significantly when it was inverted for fictive feedbacks, assuming that counterfactual thinking was employed (by multiplication with −1) before contrasting. Contrasts for alpha and switch regressors did not reveal significant differences between both conditions. Artifact-free raw EEG was averaged from 370 to 430 ms at electrode (Pz) that showed the biggest overlap between effects of the switch, PE, and learning rate predictors in the regression analysis (Figures 4C, 4D, and S4) and SDC effects locked to stimulus onset. As we observed a positive covariation in the regression analysis for switching behavior, we hypothesized that higher EEG amplitudes should be associated with a higher likelihood to switch. Additionally, because the absolute EEG amplitudes differed between both conditions (Figure 3), the analyses for real and fictive feedback were performed separately.

During the former, the depolarizing phase, neurons become increasingly sensitive to excitatory input and emit spikes. During the following hyperpolarizing phase, they are exposed to massive

inhibition by synchronously bursting inhibitory interneurons, emit no spikes, and are little susceptible to excitatory LY2157299 inputs (Wang and Buzsáki, 1996; Whittington et al., 1995). Thus, by adjusting oscillation frequency and phase of anatomically connected cell clusters, effective coupling between these clusters can be enhanced by assuring that the respective excitatory inputs are synchronized and arrive at the peak of susceptibility while coupling can be virtually abolished if phase relations among the oscillating clusters are such that excitatory volleys arrive during the phase of low susceptibility (Fries, 2005; Womelsdorf et al., 2007). Experimental and theoretical evidence indicates that the networks of mutually interacting GABAergic interneurons are crucially check details involved as pacemakers in the generation of high-frequency oscillations in local circuits (Traub et al., 2004;

Wang and Buzsáki, 1996). GABAergic interneurons, especially those expressing the calcium binding protein parvalbumin (PV), play a particularly important role in the generation of high-frequency oscillations because of their fast-spiking characteristics and the short time constants of synaptic interactions mediated by these cells (Bartos et al., 2007). In a landmark paper, Sohal Etomidate and colleagues (2009) probed the influence of up- and downregulation of PV interneurons on gamma-band oscillations in mice. Inhibition of PV interneurons led to an immediate suppression of 30–80 Hz oscillations while 10–30 Hz oscillations increased in power. In contrast, increasing PV-interneuron-mediated feedback inhibition by boosting principal cell activity enhanced gamma-band power (Cardin et al., 2009). Recent studies have also examined the specific

role of glutamatergic inputs to PV interneurons for the generation of coordinated network activity. Carlén et al. (2012) examined the effect of deleting NMDA NR1 receptors on PV interneurons applying an optogenetic approach. Mice with a reduced expression of NR1 subunits were characterized by increased spontaneous 36–44 Hz activity in somatosensory cortex compared to control animals while showing reduced gamma-band activity during sensory stimulation which was accompanied by dysfunctions in habituation, working memory, and associative learning. Optic stimulation of PV interneurons revealed diminished spike synchronization as well as increased spike latency and variance in spike timing. Similarly, Belforte et al. (2010) showed that NR1 deletion in GABAergic interneurons resulted in increased firing of pyramidal cells and reduced synchronization of neuronal responses in slices, suggesting that NMDA-receptor hypofunctioning is associated with impaired temporal coordination of neuronal activity.

These peptides elicit “late slow” depolarization that lasts minutes (Jan and Jan, 1982 and Kuffler and Sejnowski, 1983). In addition to suppressing

K+ currents such as the M-current, the neurotransmitters can also excite the neurons through activation of a Na+-dependent basal cation current that is apparently carried by the basal Na+ leak conductance (Brown and Adams, 1980, Jones, 1985 and Kuba and Koketsu, 1978). Such a mechanism of excitation through the activation of Na+-leak-like basal conductances has also been found in the excitation of serotonin neurons in the dorsal raphe nucleus by orexin (Liu et al., 2002), VTA dopaminergic neurons by SP and neurotensin Selleckchem ZVADFMK (Farkas et al., 1996), locus coeruleus neurons by SP and muscarine (Shen and North, 1992a and Shen and North, 1992b), and pre-Bötzinger complex neurons by serotonin

and SP (Peña and Ramirez, 2004 and Ptak et al., 2009). Similarly, suppression of a Na+ leak-like current can lead to hyperpolarization by driving the RMP toward EK, as suggested in the gastrin-releasing peptide containing retinorecipient neurons in the suprachiasmatic nucleus PFI-2 nmr (SCN). In these neurons, a one-hour light exposure causes a large reduction (>100 pA) of what appears to be a Na+-leak current and a hyperpolarization of membrane potential by 15 mV (LeSauter et al., 2011). Background Na+ leak conductances are also implicated in the generation and/or maintenance of spontaneous firing of neurons. Neurons with autonomous firing have been

found in many regions in the nervous systems (Häusser et al., 2004 and Llinás, 1988). The ability to generate rhythmic firing in some neurons is clearly the cell’s intrinsic property as it persists in dissociated neurons in culture and in slices when synaptic transmission is blocked. Subthreshold conductances such as the TTX-sensitive persistent Na+ conductance, resurgent Na+ conductance, voltage-activated Ca2+ channels and Ih have been shown to be the major determinants in the autorhythmicity in many neurons such as cerebellar Purkinje neurons (Raman Carnitine palmitoyltransferase II and Bean, 1997 and Raman et al., 2000) and substantia nigra pars compacta neurons (Chan et al., 2007, Guzman et al., 2009 and Puopolo et al., 2007). In some neurons such as the cerebellar nuclei neurons (Raman et al., 2000), cerebellar unipolar brush cells (Russo et al., 2007), SCN neurons (Jackson et al., 2004), dopaminergic VTA neurons (Khaliq and Bean, 2010), and substantia nigra pars reticulata neurons (Atherton and Bevan, 2005), the autonomous firing also involves conductances similar to the TTX-insensitive background Na+-leak conductance. The presence of such a conductance is proposed to set the “resting” membrane potential close to the threshold (for example −50 mV) above which voltage-sensitive channels are activated, or to depolarize the cells to the threshold potential during inter-spike interval (Khaliq and Bean, 2010).

Electrophysiological mapping revealed the orderly digit topography in area 3b and area 1 (Figure 5A). Consistent with our previous studies (Friedman et al., 2004, 2008), optical imaging of cortical activation in response to stimulation of single digit tips revealed two activation spots, one in area 3b and one in area 1 (response to D2 stimulation shown in Figure 5B). A focal injection of BDA confined to the single digit-tip representation (<500 μm in diameter)

(Figure 5C) was made in the D2 tip location, and the resulting cellular label was reconstructed (Figures 5D and BMS354825 6). The injection resulted in heavy labeling of cells (orange and yellow) near the injection site in area 3b, as well as patchy label (green and blue) distant from the injection site in the hand area in area 3b (Figure 5D; see also Figure 6). These included adjacent digit locations within area 3b in distal D1, D3, and D4. Heavy label was also observed in area 1, predominantly in the D2/D3 region with heavy focus in the tip representation zone (Figure 5D). Consistent with reciprocal connectivity patterns in somatosensory cortex, BDA-labeled axonal terminal patches (Figure 6) were also observed to share a similar pattern of connectivity (Négyessy et al.,

2013). Thus, the labeling in this case suggests topographically widespread inputs from other digit locations within area 3b, and relatively mediolaterally restricted inputs from area 1, from largely topographically matched locations. This differential intra- versus interareal Nintedanib solubility dmso pattern of labeling to was also seen in two other cases (Figures 5E and 5F; see also Figure 6). Thus, anatomical connections were characterized by two primary axes of information flow (broad intra-areal [Figures 5D–5F, curved red arrows] and comparatively focused interareal connectivity [Figures 5D–5F, straight red arrows]). This pattern was consistent

with the strong digit-matched resting-state connectivity between area 3b and area 1, the weaker but distinct connectivity between different digits within area 3b, and the even weaker connectivity between nonmatching digits between area 3b and area 1 (Figure 3E). These patterns of connectivity within area 3b and between areas 3b and 1 also were supported by electrophysiological recordings of steady-state neuron-neuron interactions in four other squirrel monkeys. After optical imaging and electrophysiology mapping (Figures 7A and 7B), on separate electrodes, single units were isolated in the digit-tip representations (D2, D3, and D4 tips) of area 3b and area 1. Area 3b-area 1 (A3b-A1) pairs were either same-digit or adjacent-digit pairs; area 3b-area 3b (A3b-A3b) pairs were all adjacent-digit pairs.

8% ( Daneshvar et al., 2009). In a recent post-mortem analysis of a patient who died of P. knowlesi, some evidence for parasite sequestration CHIR-99021 chemical structure in the brain, as described for P. falciparum, was found ( Cox-Singh et al., 2010). In vivo, P. knowlesi responds to chloroquine ( Daneshvar et al., 2010). In a prospective evaluation of oral chloroquine and primaquine therapy in patients admitted in Sarawak, with PCR-confirmed single P. knowlesi infection, oral chloroquine was given for three days followed by, at 24 h, oral primaquine for two consecutive days. Of 73 patients recruited, 60 completed follow-up over 28 days. The median fever

clearance time was 26.5 h (inter-quartile range: 16–34). The mean parasite clearance time to 50% (PCT50) and 90% (PCT90) were 3.1 h (95%

confidence interval (CI): 2.8–3.4) and 10.3 h (95% CI: 9.4–11.4), respectively. These clearance times were more rapid than in a comparison group of 23 patients with vivax malaria. No P. knowlesi recrudescences or re-infections were detected by PCR. Therefore, in Sarawak chloroquine plus/minus primaqine is an inexpensive and highly effective treatment for uncomplicated P. knowlesi malaria infections. Primaquine is used as a gametocytocidal agent PFT�� to reduce transmission. However, with both chloroquine resistant P. falciparum and P. vivax in Borneo, misidentification of P. falciparum and P. vivax as P. knowlesi, or cryptic mixed infection could have dire consequences for the patient. Other antimalarials that have been used successfully in P. knowlesi malaria include mefloquine, quinine, atovaquone/proguanil and sulphadoxine-pyrimethamine ( Daneshvar et al., 2010). The artemisinin derivatives are likely to be highly effective but formal proof of this is awaited. In Peninsular Malaysia in the 1960s Anopheles hackeri was identified as the vector for P. knowlesi. As this mosquito is predominantly zoophagic and feeds mainly at the canopy level ( Cox-Singh and Singh, 2008) it was not thought to be important for transmission to humans-who rarely visit the forest canopy. However, recent work from Sarawak suggests that P. knowlesi

malaria is transmitted to humans from long-tailed (Macaca fasicularis) and pig-tailed (M. nemestrina) macaques by Anopheles latens mosquitoes when humans visit forested areas ( Vythilingam et al., 2006 and Tan Ketanserin et al., 2008). Tan et al. (2008) demonstrated that A. latens mosquitos were attracted to both humans and caged monkeys (probably Macaca fasicularis) and that forest-caught A. latens contained P. knowlesi sporozoites. Old World monkeys are conventionally divided into two subfamilies, the Colobinae and Cercopithecinae and both taxa contain diverse species in SE Asia. P. knowlesi has been found in the cercopithecine monkeys M. fasicularis and M. nemestrina and in a colobine monkey—the banded leaf monkey (Presbytis melalophos). However, there appears to be only one report of P.

Conversely, a value of zero indicates that responses to the distracter were always greater than responses to the target. The auROC values for individual neurons were calculated for a 10 ms window from 0 ms to 600 ms from color-change onset slid in 1 ms increments along the spike train. When calculating auROC values for different distances, we corrected

for different number of trials in the conditions through a randomization procedure (see above). For each unit, the auROC values were then plotted as a function of time to describe the time course of neuronal choice probability. The latency with which neurons could distinguish the target from the distracter was defined as the time from color-change onset when the auROC time series reached the criterion value of 0.64. This value is lower than the one used in FEF studies of target selection (0.75) (Thompson et al., 1996) but is substantially higher than 0.5, which is the chance level. The latter learn more has been used in studies of dlPFC neurons’ ability to encode rules (Bongard and Nieder, 2010). However, when increasing our threshold ZD1839 cell line to 0.75 or lowering

it to 0.5, the number of neurons reaching the threshold for each distance decreased or increased, respectively, but the relative proportion across distances remained similar. The accuracy of the neuronal decision was the maximum amplitude of the time series. To obtain the estimates of latency and amplitude for the sample of neurons, we excluded cells that failed to yield latency values for at least one of the three distances. To quantify changes in response to targets and distracters during time periods following stimulus onset and around the color-change onset, we computed an MI [MI = below (Rbin − Rbl)/(Rbin + Rbl)] during both task and fixation trials. Rbin are responses during the period from stimulus onset to 400 ms after or from 200 ms prior to color-change onset to 400 ms after, computed in bins of 10 ms and increments of 1 ms. Rbl was defined as the mean

activity within a 300 ms time period immediately preceding the stimulus onset (baseline period). This allowed us to track the MIs across time and compare modulation in the main task with fixation. MI values of zero indicate similar responses; indices between 0 and 1 indicate an enhancement relative to the prestimulus-onset baseline period, whereas values between 0 and −1 point to a relative decrease. We computed Student’s t tests in bins of 50 ms and increments of 1 ms while correcting for different number of trials, and tested for significant differences between responses and baseline (evaluated at Bonferroni-corrected p < 0.05/number of comparisons across time; Figure 5, lower panels). This work was supported by grants to J.M.-T. from the CIHR, NSERC, EJLB Foundation, and Canada Research Chair program. The CIHR Canada Graduate Vanier Scholarship supported T.L. We acknowledge Mr. Walter Kucharski and Mr.

, 1989). This transport inhibition is due to the action of alkylating sulfhydryl groups on motors that profoundly alters their interactions with ATP and microtubules, stalling vesicle-motor complexes (Pfister et al., 1989). Without knowledge of the specific anterograde and retrograde motor(s) moving the assortment of cytosolic proteins, NEM provides a useful tool to examine the role of molecular motors in generating the intensity-center shift of www.selleckchem.com/products/cx-5461.html synapsin and CamKIIa in our imaging experiments. Upon the addition of 0.5 μM NEM, axonal transport of APP vesicles

was gradually perturbed and vesicular transport completely ceased at 20 min (Figure 3A). Accordingly, for our experiments we CT99021 visualized synapsin and CamKIIa transport after 10 min of NEM treatment. We found that such NEM

treatment resulted in the accumulation of stationary puncta within axons with a dramatic inhibition of the anterogradely biased motion (Figure 3B). NEM treatment did not lead to any discernible changes in the diffusion kinetics of untagged PAGFP (Figures S2C and S2D). Addition of nocodazole, a microtubule-depolymerizing agent, also resulted in an inhibition of the anterograde bias (Figure 3C and Figure S3A) as did the cellular metabolic poison (oxidative phosphorylation uncoupler) 2,4-dinitrophenol (2,4-DNP) (Figure S3B). Finally, coexpression of headless Farnesyltransferase kinesins 1A, 1B, and 1C (also called Kif-5A, Kif-5B, and Kif-5C) known to act in a dominant negative fashion

to inhibit kinesin-1 mediated transport (Kozielski et al., 1997 and Uchida et al., 2009) also inhibited the anterograde bias of synapsin (Figure S3C), further suggesting that the bias is dependent on the activity of motors. The above experiments show that populations of synapsin and CamKIIa move along axons with a motor-dependent anterograde bias. This movement seems different from fast transport, where individual vesicles are stochastically transported by molecular motors. What is the underlying molecular basis for this unconventional motion of cytosolic protein populations? We reasoned that the overall biased transit of these proteins is ultimately mediated by the movement of particles that are transport competent. First, when neurons are stained for endogenous cytosolic synaptic proteins, particulate structures are seen within naive axons (Figure S1A, and also see Fletcher et al., 1991, Roy et al., 2007 and Withers et al., 1997), suggesting that these are the native structural form of cytosolic proteins within axons. Second, particulate structures are also clearly present within photoactivated zones in our experiments (note vertical lines in kymographs, Figure 1A and elsewhere). Third, when the anterograde bias of the photoactivated pool was inhibited with NEM or nocodazole, stalled particles are seen in axons (Figure 3B and Figure S3A).