Cerebral hypoperfusion resulting in neurological symptoms can be caused by inadequate patency of supply vessels, as occurs in cerebral angiopathies of large supply arteries when affected by atherosclerosis or in small vessel disease in the context of hypertension, diabetes mellitus, or CADASIL (Moskowitz et al., 2010). Brain hypoperfusion MDV3100 cost due to vascular

abnormalities can also occur in neurodegenerative disorders such as AD, ALS, and Parkinson’s disease (PD) (Zlokovic, 2008). However, the causative nature of these vascular alterations has been debated in the past: do vascular defects cause neurodegeneration and/or accelerate disease progression, or are they a consequence of neuronal loss and cerebral hypometabolism. At least some studies have been instrumental in revealing a causal link. First, VEGF∂/∂ mice with reduced VEGF levels suffer adult-onset motoneuron degeneration, reminiscent of ALS (Oosthuyse et al., 2001 and Ruiz de Almodovar et al., 2009). The CNS of VEGF∂/∂ mice is hypoperfused, likely due to a lack of EC survival signaling (Lee et al., 2007). It remains, however, unresolved whether and how hypoperfusion occurs prior to neuronal loss, and what precisely the click here relative role is of hypoperfusion versus reduced VEGF-mediated neuroprotection (Figure 6). Second, a reduction in brain perfusion and vessel density in PDGFRβ mutant mice or in mice lacking Meox2 (Mesenchyme Homeobox

2, a transcription factor regulating vascular differentiation) results in neuronal loss and cognitive impairment (Bell et al., 2010) (Figure 5). Also noteworthy, vascular dysfunction is present early

in neurodegenerative and diseases, even prior to onset of neuronal death (Garbuzova-Davis et al., 2011 and Iadecola, 2010), implying that vascular abnormalities actively contribute to neurodegeneration. Whether hypoperfusion in neurodegeneration is due to insufficient angiogenic signaling and if so, which molecules are at play remains largely outstanding. In AD, besides perturbing ECs structurally and functionally by causing oxidative stress, Aβ squelches VEGF, inhibits VEGF binding to its receptor, suppresses EC mitogenic and survival responses to VEGF and FGF2, and induces EC autophagy, senescence, and apoptosis (Donnini et al., 2010 and Patel et al., 2010). AD patients have reduced levels of endothelial progenitor cells, implicated in repairing damaged endothelial lining. Subnormal VEGF levels in AD patients might aggravate vascular insufficiency, but elevated VEGF levels have been also documented, presumably in an effort to compensate for impaired VEGFR2 signaling (Ruiz de Almodovar et al., 2009). Not only ECs are targeted in AD, since Aβ deposits have been also detected around degenerating pericytes and SMCs, but to what extent dysfunctional mural cells causally contribute to AD’s pathogenesis remains outstanding.

Mice were anesthetized with 120 mg/kg ketamine plus 8 mg/kg xylazine (Phoenix Pharmaceuticals, St. Joseph, MO) diluted in sterile saline. Animals were placed in a stereotaxic frame with the top of the head resting 30cm below the X-ray source. A lead shield protected the body of the animals. Animals (n = 5) were exposed to cranial irradiation using a Siemens Stabilopan X-ray system operated at 300 kVp and 20 mA.

X-rays were delivered one time for 5.5 min, resulting in a dose of approximately 10 Gy. Dosimetry for this system has been reported elsewhere EGFR inhibitor (Santarelli et al., 2003). Sham-irradiated controls (n = 2) received anesthesia only. Animals were anesthetized as above and transcardially perfused with 4% paraformaldehyde (PFA). Brains were postfixed in 4% PFA overnight, cryoprotected in 30% sucrose, cryosectioned at 40 μm, and stored in PBS with 0.02% sodium azide. Free-floating sections were washed in PBS, blocked and permeablized in 10% normal donkey serum and 0.5% Triton X-100, and incubated overnight at 4°C in primary antibodies (except BLBP, 36 hr, and Nestin, 7 days) in blocking solution. For Nestin and BrdU, sections were mounted onto slides and antigen retrieval was performed. learn more The following antibodies were used: Rabbit anti-BLBP (1:1000, gift from Dr. Nathaniel

Heintz); Rat anti-BrdU (1:100, Serotec, Martinsried, Germany); Mouse anti-Calbindin (1:5000,

Swant, Bellinzona, Switzerland); Rabbit anti-Cleaved Caspase-3 (1:500, Cell Signaling Technology, Beverly, MA); Goat anti-Doublecortin (1:500, Santa Cruz Biotechnology, Santa Cruz, CA); Rabbit anti-GFAP (1:1000, DAKO, Carpinteria, CA); Chicken anti-GFP (1:500, AbCam, Cambridge, MA); Rabbit anti-GFP (1:1000, Molecular Probes, Eugene, OR); Goat anti-MCM2 (1:100, Santa Cruz Biotechnology, Santa Cruz, CA); Mouse anti-NeuN (1:1000, Chemicon, Temulca, CA); Rabbit anti-S100β (1:5000, Swant, Bellinzona, Switzerland); Rat anti-Nestin (1:50, BD PharMingen, San Diego, CA); and Rabbit anti-Tbr2 (1:1000, AbCam, Cambridge, MA). All fluorescent PD184352 (CI-1040) secondary antibodies were obtained from Jackson ImmunoResearch (West Grove, PA) and diluted 1:400 in PBS except Goat anti-Rabbit Alexa 405 (1:200, Molecular Probes, Eugene, OR). Some sections were counterstained with Hoechst 33342 (1:10,000, Molecular Probes, Eugene, OR). For quadruple labeling, sequential secondary incubation was used to avoid cross-reactivity between Goat anti-Rabbit Alexa 405 with Goat anti-Doublecortin. The Cleaved Caspase-3 antibody was visualized using ABC and a DAB kits (Vector Laboratories, Burlingame, CA). Fluorescent confocal micrographs were captured with an Olympus IX81 confocal microscope equipped with a 405 laser and the aid of Olympus Fluoview 1000v1.5 software. Representative images were edited using Adobe Photoshop.

Group data for all outcomes for the experimental and control interventions are presented in Table 2, while individual data are presented in Table 3 (see eAddenda for Table 3). The weight of the aspirate was significantly

greater after physiotherapy in the experimental group, compared to baseline. However, the control group also showed Osimertinib molecular weight a small increase and overall the difference in effect between the experimental and control groups was not statistically significant, mean difference 0.4 g (95% CI −0.5 to 1.4). After the interventions, peak airway pressure did not significantly differ between the experimental and control groups. Tidal volume was significantly greater after physiotherapy in the experimental group, compared to baseline. However, the control group also showed a small increase and overall the difference in effect between the experimental and control groups was not statistically significant, mean difference 22 mL (95% CI −20 to 65). Similarly, dynamic compliance improved significantly after physiotherapy in the experimental group, but the change was not significantly greater

than in the control group, mean difference 1 cmH2O (95% CI −3 to 4). Heart rate increased significantly in both groups from baseline, but the between-group difference in this change was not statistically significant. The changes in respiratory rate were clinically unimportant, with no statistically significant difference between the groups in the change during the intervention, mean difference 2 breaths per minute (95% CI −4 to 1). EGFR inhibitor drugs The changes in mean arterial pressure and oxyhaemoglobin saturation were also not statistically significantly different between the experimental MTMR9 and control groups. Several authors have described the use of hyperinflation to prevent lung collapse, re-expand atelectatic areas, increase oxygenation, improve lung compliance and facilitate the movement of secretions from the small to the larger central airways (Denehy

1999, Savian et al 2006, Singer et al 1994). These effects appear to occur due to an increase in the tidal volume – generated by the hyperinflation that further expands the normal alveoli through the interdependence mechanism, which also re-expands collapsed alveoli (Stiller 2000). Lemes and colleagues (2009) provided data to support this using a randomised crossover trial. A ventilator-induced increase in pressure support improved the volume of secretions aspirated and the static compliance of the respiratory system. Although the difference in the intervention arms in both the Lemes study and the current study was the use of ventilator-induced hyperinflation, the other interventions applied to both groups differed. In the Lemes study, positioning was the only other intervention. In the current study, both groups received positioning and chest wall compression with vibrations.

This loss of inhibition facilitates competition-driven spine turnover on layer 5 pyramidal cells, presumably

through the loss of inhibitory synapses on these cells (Figures 3C–3E). The reduced inhibition outside the LPZ and the resulting changes in activity levels may trigger layer 2/3 cells to extend their axons along the gradient of reduced inhibition leading into the LPZ. These axons would thus provide novel inputs that facilitate functional reorganization after a retinal lesion. Together these data suggest a critical role for inhibitory structural changes in the initiation of circuit reorganization. All AZD2281 experimental procedures were carried out in compliance with the institutional guidelines of the Max Planck Society and the local government

(Regierung von Oberbayern). The left retinae of ketamine/xylazine anaesthetized adult mice were focally photocoagulated with multiple confluent lesions (300 μm, 500–600 mW, 200 ms, corresponding to 10–15 degrees vertically and 20–40 degrees horizontally of visual angle) through a laser-adapted operating microscope, as described previously (Keck et al., 2008). In a separate group of mice, both retinae were photocoagulated in their entirety by multiple confluent laser lesions, 300 μm, BLU9931 clinical trial 700–950 mW, 200 ms, directly aimed to and surrounding the optic disc in concentric circles to destroy all retinal ganglion cell fibers. Intrinsic imaging was used to determine the location of the LPZ following focal retinal lesions. Details of the imaging procedures and visual stimulation are described elsewhere (Mrsic-Flogel et al., 2005 and Schuett et al., 2002). Briefly, the visual cortex was illuminated with 707 nm light and images (600 ms in duration) were captured with a cooled

slow-scan CCD camera (ORA 2001, Optical Imaging, Rehovot, Israel), focused 200–300 μm below the cortical surface. During each 9 s stimulation trial, four blank frames were acquired, followed by visual stimulation, during which 11 frames were acquired. Visual stimuli consisted of square-shaped gratings (10–25°) presented MYO10 at 24 positions on a screen located in the contralateral visual hemifield. Retinotopic maps were computed as previously described (Keck et al., 2008, Mrsic-Flogel et al., 2005 and Schuett et al., 2002). We implanted cranial windows (Holtmaat et al., 2009) in ketamine/xylazine-anesthetized adult GAD65-GFP transgenic mice (age at surgery, 80–100 days), which express enhanced GFP under the GAD 65 promoter (López-Bendito et al., 2004). The skull overlying the right visual cortex was removed and replaced with a cover-glass window, leaving the dura intact. Animals recovered from surgery for at least 30 days before imaging started. We carried out two-photon imaging (Denk et al.

Targeted

genome modification of hIPSCs using engineered constructs like zinc-finger nucleases (ZFNs) (Kim et al., 1996 and Porteus, 2010), transcription activator-like effector nucleases (TALENs) (Bedell et al., 2012 and Christian et al., 2010) and, more recently, clustered regularly interspaced palindromic repeats/CRISPR-associated (CRISPR/Cas) system (Mali et al., 2013 and Wiedenheft et al., 2012) present promising strategies for modeling monogenic and genetically defined disorders with reduced variability by generating isogenic control lines harboring defined genetic alterations (Soldner et al., 2011). For AZD6738 purchase modeling sporadic diseases or complex neuropsychiatric disorders where there is no clear genetic etiology, the value of these targeted genomic approaches is less clear but still likely important. It is conceivable that identifying protocols that generate lineage-specific cells will solve this problem by allowing investigators to monitor the differentiation process more specifically. Defining and consistently obtaining

the disease-relevant neural cells at comparable levels of maturation should greatly reduce the phenotypic variability and highlight pertinent disease characteristics. Assessing neuronal network connectivity formation is important for understanding neuronal communication imbalance in disease but can be a challenging task because, as a general rule, the right subtype of neurons and the specific maturation time are not selleck products represented in the dish at appropriate levels. To that end, designing cell-type-specific promoters may help in generating the desired populations of neurons that are directly involved in the disease

being studied (for example, Hb9-positive cells for diseases involving alpha motor neurons such as ALS [Marchetto et al., 2008]). Additionally, single-cell expression profiling should further clarify the levels of population heterogeneity within in vitro cultures, and advances in media culture platforms and automated cell processing should provide the desired accuracy and consistency that will be required. L-NAME HCl For a number of neurological diseases, it remains unclear whether the phenotypes involved in the pathology are restricted to the neuronal population and to what extent the neighboring cells are also playing a major role. Improving the protocols for generation of cells present in the neuronal niche (i.e., astrocytes, oligodendrocytes, microglia, and endothelial cells) could reveal important disease phenotypes and contribute to the development of alternative therapies. Refining the techniques to analyze neuronal phenotypes will also help to detect more subtle differences.

, 2000). In particular, constitutive TNFα has recently been implicated in control of the stability of neuronal networks in response to prolonged changes in activity via the phenomenon of synaptic scaling (Stellwagen and Malenka, 2006 and Turrigiano, 2008) and plays a specific role in ocular dominance plasticity upon monocular visual deprivation (Kaneko et al., 2008). The cytokine, released from astrocytes, was reported to strengthen excitatory synaptic transmission by promoting surface insertion of AMPA receptor (AMPAR) subunits GABA agonists list (Bains and Oliet, 2007, Beattie et al., 2002 and Stellwagen et al., 2005). In the present study, we find that TNFα is also an obligatory factor for the induction

of synaptically effective gliotransmission EGFR inhibitor review at GC synapses in the dentate gyrus, specifically controlling glutamate release from astrocytes. Notably, constitutive levels of the cytokine promote functional docking and rapid coordinated secretion of glutamatergic vesicles in cultured astrocytes. Indeed, TNFα most likely determines the kinetics of

P2Y1R-dependent glutamate release in situ and the local extracellular concentration of the amino acid, a critical factor in the activation of pre-NMDAR and, ultimately, in the potentiation of GC synapses. To investigate the role of TNFα in astrocyte-dependent synaptic modulation in hippocampal dentate gyrus, we planned studies on Tnf−/− mice ( Pasparakis et al., 1996). Our previous work in rats established that purinergic P2Y1R, strongly expressed in astrocytic processes around GC synapses, respond to stimulation with the agonist 2-methylthioadenosine-5′-diphosphate (2MeSADP, 10 μM) ADP ribosylation factor by inducing a highly reproducible increase in mEPSC frequency in GCs ( Jourdain et al., 2007). We therefore decided to utilize this stimulus paradigm and recorded mEPSCs from hippocampal dentate GCs in acute mouse hemibrain horizontal slices

from P18–P23 mice. Recordings were performed 50–90 μm deep in the slices, where astrocytes and the patched GCs retained their integral tridimensional structures, as confirmed by two-photon imaging of cells fluorescently labeled with specific markers ( Figure 1A). Initially, we used slices from wild-type (WT) mice and applied 2MeSADP either by bath perfusion or locally, within the volume of the recorded GC, via pressure ejection from a micropipette. In both cases the P2Y1R agonist increased mEPSC frequency in GCs (bath application: +37% ± 11%; p < 0.05; n = 14 cells; local application: +32% ± 10%, p < 0.05; n = 7 cells), with no effect on the amplitude or kinetics of the currents ( Figure 1B and see Figure S1 available online). The effect of the drug on mEPSC frequency was abolished in the presence of N6-methyl-2′-deoxyadenosine-3′,5′-bisphosphate (MRS2179 10 μM; n = 7 cells), a P2Y1R blocker, confirming the specific involvement of this purinergic receptor subtype.

How do these IT neuronal population phenomena (above) depend on the responses of individual IT neurons? Understanding IT single-unit responses has proven to be extremely challenging and while some progress has been made (Brincat and Connor, 2004 and Yamane et al., 2008), we still have a poor ability to build encoding models

that predict the responses of each IT neuron to new images (see Figure 4B). Nevertheless, we know that IT neurons are activated by at least moderately complex combinations of visual features (Brincat and Connor, 2004, Desimone et al., 1984, Kobatake and Tanaka, 1994b, Perrett et al., 1982, Rust and DiCarlo, FK228 order 2010 and Tanaka, 1996) and that they are often able to maintain their relative object preference over small to moderate changes in object position and size (Brincat and Connor, 2004, Ito et al., 1995, Li et al., 2009, Rust and DiCarlo, 2010 and Tovée et al., 1994), pose (Logothetis et al., 1994), illumination (Vogels and Biederman, 2002), and clutter (Li et al., 2009, Missal et al., 1999, Missal et al., 1997 and Zoccolan et al., 2005). Contrary to popular depictions of IT neurons as narrowly selective “object detectors,” neurophysiological studies of IT are in selleck chemicals near universal agreement with early accounts that describe a diversity of selectivity: “We found that, as in other visual areas, most IT neurons respond to many different

visual stimuli and, thus, cannot be narrowly tuned ‘detectors’ for particular complex objects…” (Desimone et al., 1984).

For example, studies that involve probing the responses of IT cells with large and diverse stimulus sets show that, while some neurons appear highly selective for particular objects, they are the exception not the rule. Instead, most IT neurons are broadly these tuned and the typical IT neuron responds to many different images and objects (Brincat and Connor, 2004, Freedman et al., 2006, Kreiman et al., 2006, Logothetis et al., 1995, Op de Beeck et al., 2001, Rolls, 2000, Rolls and Tovee, 1995, Vogels, 1999 and Zoccolan et al., 2007; see Figure 4B). In fact, the IT population is diverse in both shape selectivity and tolerance to identity-preserving image transformations such as changes in object size, contrast, in-depth and in-plane rotation, and presence of background or clutter (Ito et al., 1995, Logothetis et al., 1995, Op de Beeck and Vogels, 2000, Perrett et al., 1982, Rust and DiCarlo, 2010, Zoccolan et al., 2005 and Zoccolan et al., 2007). For example, the standard deviation of IT receptive field sizes is approximately 50% of the mean (mean ± SD: 16.5° ± 6.1°, Kobatake and Tanaka, 1994b; 24.5° ± 15.7°, Ito et al., 1995; and 10° ± 5°, Op de Beeck and Vogels, 2000). Moreover, IT neurons with the highest shape selectivities are the least tolerant to changes in position, scale, contrast, and presence of visual clutter ( Zoccolan et al.

While technology for such interventions is still under development, it is important that computational models spell out their predictions clearly to provide a fundament for definitive testing as soon as the methods are available. Computational models have been particularly important in the search for mechanisms of grid cells. Theoretical models have for example highlighted the potential role of multiple single-cell properties, such as oscillations and after-spike

dynamics, in grid cell formation. With the introduction of in vivo whole-cell patch-clamp and optogenetic methods, the role of these properties can be tested. Direct and controllable manipulation of intrinsic oscillation frequencies, the timing of synaptic http://www.selleckchem.com/products/abt-199.html inputs, or the spiking dynamics of identified grid cells would provide paramount insight into what mechanisms contribute to the formation of spatially responsive neurons. Similarly, network models make strong assumptions about the architecture of

the grid cell circuit, but whether www.selleckchem.com/products/pifithrin-alpha.html the wiring has a Mexican hat pattern or whether connections are circular are examples of questions that cannot be tested until connections between functionally identified neurons can be traced at a large scale. It is possible that a combination of virally based tagging methods and voltage-sensing optical imaging

approaches may get us to this point in the not-too-distant future. Computational models have also offered potential mechanisms for transformation of spatial signals between subsystems of the entorhinal-hippocampal circuit. Current models provide a starting point, for example, for testing hypotheses of how a periodic entorhinal Metalloexopeptidase representation might transform into a nonperiodic hippocampal representation. With emerging technologies such as optogenetics (Yizhar et al., 2011) and virally based tagging (Marshel et al., 2010), it will soon be possible to address the functions of specific inputs to the hippocampus, for example by manipulation of specific spatial wavelengths of the grid signal. New studies will also improve our understanding of interactions that occur within individual brain regions. Anatomical evidence now strongly hints at a modular organization of entorhinal cortical neurons. But what physiological properties or cell types would the anatomical modules correlate with, and how would the individual modules interact to form a cohesive representation of the environment? Existing computational models consider only one or two cell types at most, and none of the current models integrate outputs from border cells, grid cells, and head direction cells.

For example, introduction of the H134R mutation into ChR2 was found to be of mixed impact, improving currents ∼2-fold during prolonged stimulation although at the GW786034 expense of ∼2-fold slower channel-closure kinetics and consequent poorer temporal precision (Nagel et al., 2005 and Gradinaru et al., 2007); nevertheless, like hChR2, hChR2(H134R) can drive precise low-frequency spike trains

within intact tissue and is widely used. Similarly, modification of the Thr159 position (T159C; Berndt et al., 2011) and the Leu132 position (L132C; Kleinlogel et al., 2011) were found to increase photocurrent magnitude with a concomitant slowing in channel off-kinetics. Notably, modified ChRs have been developed with a chimera-based approach (Wang et al., 2009, Lin et al., 2009 and Yizhar et al., 2011a), resulting in both quantitatively Nintedanib ic50 stronger photocurrents and reduced desensitization in cultured neurons. A substantially red-shifted channelrhodopsin (VChR1) that can be excited by amber (590 nm) light, which does not affect ChR2 at all, was identified by genomic strategies and validated in cultured neurons (Zhang et al., 2008), raising the possibility of

combinatorial excitation in vivo (Yizhar et al., 2011a). Most channelrhodopsins described to date have a relatively low single-channel conductance and broad cation selectivity (Nagel et al., 2003, Zhang et al., 2008, Lin et al., 2009, Tsunoda and Hegemann, 2009 and Gunaydin et al., 2010), but cellular photocurrents can be vastly improved with molecular engineering strategies, including for VChR1 (e.g., Yizhar et al., 2011a). With the exception of the recently reported L132C mutant (Kleinlogel et al., 2011), channelrhodopsins generally give rise to only small Ca2+ currents at physiological Ca2+ concentrations, and increases in cytosolic Ca2+ due to channelrhodopsin activation result chiefly from activation of endogenous voltage-gated Ca2+ channels via membrane depolarization

and neuronal spiking (Zhang and Oertner, 2007), which also occur to varying extents with different native depolarization processes. Second- and to also third-order conductances (e.g., Ca2+-gated potassium and chloride currents) must nevertheless be kept in mind, especially when higher Ca2+-conducting channelrhodopsins are employed, as these will influence light-evoked activity in a manner that may vary from cell type to cell type; for example, different cells (or even different regions of the same cell) may elicit, tolerate, or respond to higher levels of Ca2+ differently. Recent modeling work in which photocurrent responses were integrated with a Hodgkin-Huxley neuron model (Grossman et al.

A quantification of spines added or eliminated following NgR1 knockdown revealed a significant increase in spine addition but no change in spine elimination (Figures 3G, S3A, and S3B), lending support to the idea that NgR1 functions to suppress the establishment of new synapses rather than by mediating synapse elimination. Several NgR1 ligands and coreceptors are expressed on axons and dendrites; thus, the potential exists for NgR1 to signal bidirectionally. To address whether NgR1 functions pre- or postsynaptically, we quantified changes in synapse density observed upon knockdown or overexpression of NgR1 and then

deconvolved these same data sets to determine whether there was a change in the number of pre- and/or Panobinostat price postsynaptic specializations. This analysis revealed that the effects of NgR1 on synapse Entinostat price density were due to changes in the number of postsynaptic (PSD95 or GluR2) puncta rather than the presynaptic (Syn1 or Syt1) puncta (Figures 4A–4C; data not shown). Similarly, deconvolution of synapse density measurements following RNAi targeting of NgR2 and NgR3 also revealed a specific increase in PSD95 puncta number, size, and intensity

(Figures S2G and S2H). Importantly, simulated modeling studies confirmed that the changes in synapse density following NgR1 knockdown could not be accounted for by random overlap due to increased numbers of postsynaptic puncta (Figures S4B and S4C). To determine whether changing the level of NgR1 throughout neuronal cultures affects the levels of specific

synaptic proteins, we infected neurons with lentiviruses to drive the expression of NgR1 throughout neuronal cultures and found that WTNgR1 overexpression results in second a significant reduction in PSD95 protein levels as assessed by quantitative western blotting (Figures 4D and 4E). Moreover, the opposite effect was observed upon NgR1 knockdown, which resulted in a significant increase in both PSD95 and GluR2 levels (Figures 4D, 4E, and S4A). In contrast, the level of Syn1 was unaffected by NgR1 overexpression or knockdown (Figures 4D, 4E, and S4A). Thus, analysis of both single cells and neuronal cultures suggests that NgR1 inhibits the development of excitatory synapses through its action in the postsynaptic cell, where it causes reduced expression of specific postsynaptic proteins. These findings suggest that NgR1 has a cell-autonomous role in the dendrite that is distinct from its previously described function in the axon. NgR1 functions by activating intracellular signaling cascades via transmembrane coreceptors such as P75, TROY, and Lingo-1 (Yiu and He, 2006). To investigate whether coreceptor signaling is required for the inhibition of synapse formation by NgR1, we tested the effect of an NgR1 mutant that lacks a co-receptor-binding region (DNNgR1 [Wang et al., 2002a]).