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- Life imitates op art
- Nat Neurosci 14(7):803-804 (2011)
Article preview View full access options Nature Neuroscience | News and Views Life imitates op art * Spencer L Smith1 * Ikuko T Smith1 * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:803–804Year published:(2011)DOI:doi:10.1038/nn.2865Published online27 June 2011 The beautiful, undulating orientation maps in visual cortex have motivated many developmental models. A new study finds that this functional organization could be seeded in the retina by moiré interference between mosaics of ON-center and OFF-center retinal ganglion cells. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Neuroscience for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Spencer L. Smith and Ikuko T. Smith are at the Wolfson Institute for Biomedical Research, University College London, London, UK. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Spencer L Smith Author Details * Spencer L Smith Contact Spencer L Smith Search for this author in: * NPG journals * PubMed * Google Scholar * Ikuko T Smith Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Vision and olfaction say UNC-le to G proteins
- Nat Neurosci 14(7):805-806 (2011)
Article preview View full access options Nature Neuroscience | News and Views Vision and olfaction say UNC-le to G proteins * Eyal Vardy1 * Bryan L Roth1 * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:805–806Year published:(2011)DOI:doi:10.1038/nn.2863Published online27 June 2011 Guided by novel structural insights, a study now demonstrates that UNC119 is a lipid-binding protein essential for proper trafficking of G-protein a subunits in mammalian photoreceptors and Caenorhabditis elegans sensory neurons. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Neuroscience for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Eyal Vardy and Bryan L. Roth are in the Departments of Pharmacology and Medicinal Chemistry and the Program in Neurosciences, University of North Carolina Chapel Hill Medical School, Chapel Hill, North Carolina, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Bryan L Roth Author Details * Eyal Vardy Search for this author in: * NPG journals * PubMed * Google Scholar * Bryan L Roth Contact Bryan L Roth Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Cajal revisited: does the VMH make us fat?
- Nat Neurosci 14(7):806-808 (2011)
Article preview View full access options Nature Neuroscience | News and Views Cajal revisited: does the VMH make us fat? * Chun-Xia Yi1 * Thomas Scherer2 * Matthias H Tschöp1 * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:806–808Year published:(2011)DOI:doi:10.1038/nn.2867Published online27 June 2011 A new study used several mouse mutants to study insulin receptor function specifically in the ventromedial nucleus of the hypothalamus (VMH), and found a role for VMH insulin signaling in promoting high-fat diet–induced obesity. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Neuroscience for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Chun-Xia Yi and Matthias H. Tschöp are at the Metabolic Disease Institute, Division of Endocrinology, Department of Medicine, University of Cincinnati, Cincinnati, Ohio, USA. * Thomas Scherer is in the Division of Endocrinology, Diabetes and Bone Disease, Department of Medicine, Mount Sinai School of Medicine, New York, New York, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Matthias H Tschöp Author Details * Chun-Xia Yi Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Scherer Search for this author in: * NPG journals * PubMed * Google Scholar * Matthias H Tschöp Contact Matthias H Tschöp Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Net(o) excitement for kainate receptors
- Nat Neurosci 14(7):808-810 (2011)
Article preview View full access options Nature Neuroscience | News and Views Net(o) excitement for kainate receptors * Juan Lerma1Journal name:Nature NeuroscienceVolume: 14,Pages:808–810Year published:(2011)DOI:doi:10.1038/nn.2864Published online27 June 2011 A study now shows that association of kainate receptors with the auxiliary protein Neto1 confers the slow activation and deactivation kinetics of synaptic responses, as well as the high agonist affinity seen in vivo. Article preview Read the full article * Instant access to this article: US$18 Buy now * Subscribe to Nature Neuroscience for full access: Subscribe * Personal subscribers: Log in Additional access options: * Login via Athens * Login via your Institution * Purchase a site license * Use a document delivery service * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Author information Article tools * Full text * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Juan Lerma is at the Instituto de Neurociencias de Alicante, Consejo Superior de Investigaciones Cientificas–Universidad Miguel Hernández, San Juan de Alicante, Spain. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Juan Lerma Author Details * Juan Lerma Contact Juan Lerma Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Measuring and interpreting neuronal correlations
- Nat Neurosci 14(7):811-819 (2011)
Nature Neuroscience | Review Measuring and interpreting neuronal correlations * Marlene R Cohen1 * Adam Kohn2 * Affiliations * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:811–819Year published:(2011)DOI:doi:10.1038/nn.2842Published online27 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Mounting evidence suggests that understanding how the brain encodes information and performs computations will require studying the correlations between neurons. The recent advent of recording techniques such as multielectrode arrays and two-photon imaging has made it easier to measure correlations, opening the door for detailed exploration of their properties and contributions to cortical processing. However, studies have reported discrepant findings, providing a confusing picture. Here we briefly review these studies and conduct simulations to explore the influence of several experimental and physiological factors on correlation measurements. Differences in response strength, the time window over which spikes are counted, spike sorting conventions and internal states can all markedly affect measured correlations and systematically bias estimates. Given these complicating factors, we offer guidelines for interpreting correlation data and a discussion of how best to evaluate! the effect of correlations on cortical processing. View full text Figures at a glance * Figure 1: Types of pair-wise neuronal correlations. () Tuning curves for two hypothetical direction-selective neurons. Open circles show mean responses to different directions of motion and small points show responses to individual presentations of a stimulus at a particular direction. () Spike count or 'noise' correlation (rSC) measures the correlation between fluctuations in responses to the same stimulus. Here, each point represents the response of the two neurons on one presentation of an individual stimulus. () Signal correlation (rsignal) measures the correlation between the two cells' mean responses to different stimuli. Each point represents the mean response to a given direction of motion. Because the responses of cell 2 increase of a range of motion directions in which the responses of cell 1 decline, signal correlation is negative. * Figure 2: Measured correlations are small when responses are weak. () We drew intracellular voltage events from a bivariate Gaussian distribution of voltage relative to threshold (Vm) such that the two neurons had the same mean voltage and the correlation coefficient between membrane potentials was 0.2 (left). These voltage events were converted to extracellular firing rates by passing them through a nonlinearity such that the firing rate on the ith trial fri = Vmi1.7 (center). We picked this exponent so that the variance of the output rates was approximately equal to the mean39. We defined the spike count on each trial to be equal to the firing rate rounded to the nearest whole spike (right). In the case of high mean voltages, the measured spike count correlation is close to the input correlation (rSC = 0.20). Shading indicates the number of observations. () Data are presented as in for low mean Vm. When the threshold masks subthreshold events, the measured spike count correlation (rSC = 0.04) is much lower than the membrane potential corr! elation. () Measured rSC as a function of firing rate, using the simulations in and when the two cells had the same mean rate. The circles represent the correlations and mean rates in and . () Measured rSC as a function of the firing rates of each of the two cells. We simulated responses using identical methods as those in and , but we allowed the rates of the two neurons in a pair to differ. Correlations depend more strongly on the minimum rate in the pair than the mean. * Figure 3: Counting spikes over short response windows can decrease measured correlations. () We simulated correlated spike trains as a combination of independent Poisson spike trains (black spikes in the schematic on the left, mean = 20 spikes per s) and inserted shared, synchronous spikes (red spikes, mean = 5 spikes per s). Thus, each neuron's response was the sum of independent and shared spikes (mean = 25 spikes per s). When there is no jitter in the timing of the shared spikes, the cross-correlogram has a sharp peak at 0-ms time lag (right). () Data are presented as in for jittered spike times. We simulated variable timescales by jittering the timing of the correlated spikes by an amount picked from a Gaussian distribution (left, red spikes). This results in a cross-correlogram with a peak whose width depends on the s.d. of the Gaussian distribution (right). () Measured rSC as a function of counting window for several timescales of correlation. The number next to each curve corresponds to the s.d. of the Gaussian jitter in milliseconds. * Figure 4: Measured correlations grow slowly with the number of units that contribute to multiunit activity. We simulated single unit activity by choosing random variables from a bivariate Gaussian with covariance defined to give pair-wise correlation values of 0.001–0.1. We then computed rSC between sums of these variables (as one would do when recording multiunit activity). Measured rSC increases with the number of 'units' contributing to the multiunit activity. The increase in magnitude is gradual, however, essentially proportional to the number of units contributing to the multiunit response. For instance, if the pair-wise correlations are 0.01, multiunit clusters consisting of 10–20 units would be needed to obtain rSC values in the typical range (0.1–0.2, indicated by horizontal dashed lines). * Figure 5: Spike sorting errors can reduce the strength of measured correlations. (,) Two ensembles of spike waveforms (red and gray) created by taking two differently shaped waveforms (thick lines) and corrupting them with multiplicative and additive noise. These waveforms are represented by the amplitude of their first two principal components (PCs); each dot indicates one waveform. Lighter shading indicates greater distance in principal component space from the average waveform. () Effect of increasingly stringent sorting thresholds (that is, keeping only those spikes that are in a certain distance, in principal component space, of the average waveform) on the measured correlation strength, for true correlation values of 0.05, 0.1 and 0.2. As the proportion of discarded spikes increases, the measured value of rSC decreases strongly. (,) Data are presented as in and for waveforms that form a tight cluster () and for those with a wider range of shapes (). () Measured correlation (rSC) between the well-isolated unit () and multiple random divisions of the! waveforms in . The true correlation is indicated by the filled dots to the left (0.05, 0.1 and 0.2). When the waveforms of the cell shown in are assigned arbitrarily to two units (filled symbols), measured rSC decreases by roughly 25%, compared with the true correlation between the pair. When the waveforms are arbitrarily divided among more than two units, rSC falls further. These scenarios correspond to setting p1 = 0 and p2 = 0.5 (for two units created), 0.66 (for three units created), and so on, in equation (2). * Figure 6: Differences in mean rates duration predict much of the variability in rSC measurements across studies. The figure plots average rSC as a function of mean firing rate and measurement duration for 13 studies of correlations in primate visual cortex, for which these data were either provided or could be estimated from summary figures. When a range of values was reported (for example, from different stimulus or behavioral conditions or for pairs of neurons separated by different distances), we plotted either the average or most common value, and in some cases we estimated values from summary plots. The mean firing rate accounts for 41% of the cross-study variance in mean rSC values. Differences in spike sorting were not included in this meta-analysis because sort quality is rarely quantified or discussed. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA. * Marlene R Cohen * Department of Neuroscience and Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York, USA. * Adam Kohn Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Marlene R Cohen or * Adam Kohn Author Details * Marlene R Cohen Contact Marlene R Cohen Search for this author in: * NPG journals * PubMed * Google Scholar * Adam Kohn Contact Adam Kohn Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Results Additional data - Glia instruct developmental neuronal remodeling through TGF-β signaling
- Nat Neurosci 14(7):821-823 (2011)
Nature Neuroscience | Brief Communication Glia instruct developmental neuronal remodeling through TGF-β signaling * Takeshi Awasaki1, 2 * Yaling Huang1, 2 * Michael B O'Connor3 * Tzumin Lee1, 2 * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:821–823Year published:(2011)DOI:doi:10.1038/nn.2833Received26 January 2011Accepted11 April 2011Published online19 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We found that glia secrete myoglianin, a TGF-β ligand, to instruct developmental neural remodeling in Drosophila. Glial myoglianin upregulated neuronal expression of an ecdysone nuclear receptor that triggered neurite remodeling following the late-larval ecdysone peak. Thus glia orchestrate developmental neural remodeling not only by engulfment of unwanted neurites but also by enabling neuron remodeling. View full text Figures at a glance * Figure 1: Expression of myo transcripts in the larval brain. (–) Expression of myo transcripts detected by in situ hybridization (ISH) in brains of early third (), middle third () and wandering third instar larvae (,). Cortex layer () and inner brain region () are shown. (–) Signals of myo transcripts (arrows) colocalized with the cell bodies of cortex glia () and astrocyte-like glia (), which were labeled with nuclear-lacZ (NZ, pink). Scale bars represent 50 μm. See Supplementary Methods for details of methods, fly stocks and crosses. * Figure 2: Effect of glial silencing of myo on mushroom body remodeling. (–) Remodeling of mushroom body axonal lobes during metamorphosis. Mushroom body lobes labeled with antibody to Fas2 (,) and their schematics (,) in control (,) and repo>myo RNAi (dsRNA) flies (,) at wandering larval (WL), 18 h APF and adult stages. Arrows show larval lobes of γ neurons (,). In repo>myo RNAi flies (), larval γ lobes persisted at 18 h APF (arrows with asterisks) and adult (γ*). (–) Expression of EcR-B1 in control (,,), repo>myo-miRNA (,,,,,) wandering larva. The miRNA-resistant myo was coexpressed in glia (,,). Arrows show bilateral clusters of mushroom body γ neurons (–). High-magnification view of larval mushroom body neurons stained with antibody to EcR-B1 (–). Cell bodies of mushroom body neurons were counter-labeled with MB247>rCD2::GFP in –. Scale bars, 50 μm. * Figure 3: Effect of myo loss of function on mushroom body remodeling. () Genomic organization of myo deletion mutant. () RT-PCR of myo transcript in the myoΔ1 and yellow white (yw) larvae. (–) Expression of EcR-B1 in mushroom body γ neurons in myoΔ1 white pupa (,) and prepupa (,) and control prepupa (,). Counter-labeling of cell bodies of mushroom body neurons with MB247>rCD2::GFP, –. (–) Effect of myo overexpression in the myoΔ1 mutant with (–) and without (–) glial expression. Expression of EcR-B1 in mushroom body γ neurons in white pupae (,). Mushroom body lobes of 18 h APF (,) and adult brain (,) labeled with antibody to Fas2. Myo overexpression was induced by myo-GAL4 (–); glial expression was suppressed with repo-GAL80 (–). Scale bars, 50 μm. Author information * Author information * Supplementary information Affiliations * Department of Neurobiology, University of Massachusetts, Worcester, Massachusetts, USA. * Takeshi Awasaki, * Yaling Huang & * Tzumin Lee * Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. * Takeshi Awasaki, * Yaling Huang & * Tzumin Lee * Howard Hughes Medical Institute and Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, Minnesota, USA. * Michael B O'Connor Contributions T.A. designed the study, conducted the experimental work, interpreted the data and wrote the manuscript. Y.H. and M.B.O. designed and established new transgenic lines. T.L. supervised the project and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Takeshi Awasaki or * Tzumin Lee Author Details * Takeshi Awasaki Contact Takeshi Awasaki Search for this author in: * NPG journals * PubMed * Google Scholar * Yaling Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Michael B O'Connor Search for this author in: * NPG journals * PubMed * Google Scholar * Tzumin Lee Contact Tzumin Lee Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (901K) Supplementary Figures 1–13, Supplementary Table 1 and Supplementary Methods Additional data - Synaptic vesicle retrieval time is a cell-wide rather than individual-synapse property
- Nat Neurosci 14(7):824-826 (2011)
Nature Neuroscience | Brief Communication Synaptic vesicle retrieval time is a cell-wide rather than individual-synapse property * Moritz Armbruster1, 2 * Timothy A Ryan2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:824–826Year published:(2011)DOI:doi:10.1038/nn.2828Received22 February 2011Accepted31 March 2011Published online29 May 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Although individual nerve terminals from the same neuron often differ in neurotransmitter release characteristics, the extent to which endocytic retrieval of synaptic vesicle components differs is unknown. We used high-fidelity optical recordings to undertake a large-scale analysis of endocytosis kinetics of individual boutons in hippocampal rat neurons. Our data indicate that endocytosis kinetics do not differ substantially across boutons from the same cell but instead appear to be controlled at a cell-wide level. View full text Figures at a glance * Figure 1: Variation in endocytosis across boutons. () Representative field of synaptic boutons transfected with pHluorin-tagged vGlut1. Scale bar represents 10 μm. () Example traces with exponential fits to the fluorescence decays of a single bouton stimulated with a 100–action potential, 10-Hz stimulus. Decay times are 11.5 ± 0.5 s and 6.6 ± 0.4 s, respectively. () Exo-endocytic responses from 29 consecutive runs from a single bouton with 5 min of recovery between runs. () Heat map of all endocytic decays of an individual cell, color-coded from 4 s (red) to 27 s (blue), with gray indicating events that did not pass the inclusion criteria. Boutons are sorted from fastest (left) to slowest (right), and runs are sorted from first (top) to last (bottom) (29 runs, 46 boutons, 1,136 events, average time constant = 9.2 ± 0.1 s). () Histogram of all 832 bouton events from a single cell plotted with the maximum-likelihood fit of the Markov model (histogram (black) mean <τendo> = 14.50 s; model (red) mean <τendo> = 15.22 s; N! = 19 vesicles). * Figure 2: Cell to cell variations in endocytosis. () Heat maps of three different cells (truncated to 18 runs (top to bottom), 20 boutons each). Gray represents bouton events that did not meet the inclusion criteria (average τendo: 20.7 ± 0.3 s, 6.3 ± 0.1 s, 15.7 ± 0.3 s). () <τendo> distribution across cells shows a range of 5.5–39.8 s (N = 84 cells). () Distribution and <τendo> of wild-type cells (WT), AP-2 knockdown cells (AP-2 KD), vGlut-positive cells, vGlut-positive, TTX-silenced cells (vGlut-TTX), vGat-positive cells, and vGat-positive, TTX-silenced cells (vGat-TTX) determined from post-experiment immunofluorescence staining. Normalizing the distributions to their mean revealed that there was no significant difference in the spread of the distribution for AP-2 KD cells compared to wild-type cells (Kolmogorov-Smirnov test, P = 0.29). Silencing cultures with TTX for 2–8 d revealed no significant difference in their distributions (Kolmogorov-Smirnov test; vGlut, P = 0.24; vGat, P = 0.69). vGlut and vGat segreg! ation showed no significant difference in their distributions (Kolmogorov-Smirnov test, P = 0.28). Box-whisker plots show distributions (whiskers, 5–95%; box, 25–50–75%) (wild type, N = 84; AP-2 KD, N = 14; vGlut, N = 10; vGlut-TTX, N = 14; vGat, N = 16; vGat-TTX, N = 11 cells). Cell-averaged time constants are shown (wild type, 12.3 ± 0.6 s; AP-2 KD, 35.3 ± 5.3 s; vGlut, 13.4 ± 0.8s; vGlut-TTX, 14.3 ± 1.5 s; vGat, 13.4 ± 0.9 s; vGat-TTX, 12.4 ± 0.9 s). Author information * Author information * Supplementary information Affiliations * David Rockefeller Graduate Program of Rockefeller University, New York, New York, USA. * Moritz Armbruster * The Department of Biochemistry, Weill Cornell Medical College, New York, New York, USA. * Moritz Armbruster & * Timothy A Ryan Contributions M.A. and T.A.R. designed the study. M.A. performed the experiments. M.A. and T.A.R. analyzed the data. M.A. and T.A.R. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Timothy A Ryan Author Details * Moritz Armbruster Search for this author in: * NPG journals * PubMed * Google Scholar * Timothy A Ryan Contact Timothy A Ryan Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–6 and Supplementary Methods Additional data - Light acts through melanopsin to alter retinal waves and segregation of retinogeniculate afferents
- Nat Neurosci 14(7):827-829 (2011)
Nature Neuroscience | Brief Communication Light acts through melanopsin to alter retinal waves and segregation of retinogeniculate afferents * Jordan M Renna1 * Shijun Weng1 * David M Berson1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:827–829Year published:(2011)DOI:doi:10.1038/nn.2845Received09 March 2011Accepted22 April 2011Published online05 June 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Waves of correlated activity sweeping across the early postnatal mouse retina promote the segregation and refinement of retinofugal projections. This process has been thought to be spontaneous and unaffected by visual experience. We found, however, that light prolongs spiking during the waves and enhances the segregation of retinogeniculate afferents, and that it did so by activating melanopsin-expressing, intrinsically photosensitive retinal ganglion cells. View full text Figures at a glance * Figure 1: Light increases wave duration in conventional ganglion cells. () Simultaneous loose-patch voltage-clamp recordings from two conventional ganglion cells located <150 μm apart, in the dark (top), in the light (middle) and after a 10-min period of dark recovery (bottom). Representative wave-associated bursts of spikes (arrowheads) are shown on the right at a faster time base and increased gain. Both cells were conventional ganglion cells rather than ipRGCs, as they did not express EGFP in this melanopsin reporter mouse and lacked any detectable direct photoexcitation. () Pooled data for burst duration in conventional ganglion cells recorded by this loose-patch method. Light increased wave burst duration by an average of 45% over the dark condition (*P < 0.0001, n = 9). () In a melanopsin knockout retina (Opn4−/−), wave-associated bursts were not significantly longer in the light than in the dark (P 0.05, n = 13). Error bars represent s.e.m. * Figure 2: Light enhances ocular segregation of retinogeniculate afferents by a melanopsin-dependent mechanism. () Segregation of retinogeniculate afferents in mice reared in constant light as revealed by injections of fluorescent tracer into the contralateral (green) and ipsilateral (red) eyes (horizontal plane, single optical sections). Scale bar represents 50 μm (first three columns) and 10 μm (right column). Rightmost panel shows expanded view of region in the white rectangle, the transition zone between sectors dominated by inputs from one eye. Top, wild-type (Opn4+/−) mouse. Bottom, melanopsin knockout littermate (Opn4−/−). () Distribution of pixel intensity ratios (R) for all dLGN pixels in the right dLGN of all mice (for each pixel, R is the log of the ipsilateral-eye divided by contralateral-eye signal intensity). Opn4−/− mice (blue curve) had more pixels with nearly matched strength of input from the two eyes (unsegregated as shown in gray) and fewer were strongly dominated by the ipsilateral eye than in wild-type mice (orange curve). () Pooled data across mice s! howing that the fraction of all dLGN pixels that were unsegregated (relatively balanced input from the two eyes) was higher in Opn4−/− mice (blue) than in their wild-type littermates (orange). The effect was evident on both sides of the brain. Error bars represent s.e.m. **P < 0.01, *P < 0.05. Author information * Author information * Supplementary information Affiliations * Department of Neuroscience, Brown University, Providence, Rhode Island, USA. * Jordan M Renna, * Shijun Weng & * David M Berson Contributions S.W. performed the multi-electrode array recordings. J.M.R. performed the loose-patch recordings and the anatomical tracing studies. J.M.R. carried out all data analysis for both electrophysiological and anatomical studies. J.M.R. and D.M.B. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jordan M Renna Author Details * Jordan M Renna Contact Jordan M Renna Search for this author in: * NPG journals * PubMed * Google Scholar * Shijun Weng Search for this author in: * NPG journals * PubMed * Google Scholar * David M Berson Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (4M) Supplementary Figures 1–5 and Supplementary Methods Additional data - Differential coupling of visual cortex with default or frontal-parietal network based on goals
- Nat Neurosci 14(7):830-832 (2011)
Nature Neuroscience | Brief Communication Differential coupling of visual cortex with default or frontal-parietal network based on goals * James Z Chadick1 * Adam Gazzaley1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:830–832Year published:(2011)DOI:doi:10.1038/nn.2823Received04 January 2011Accepted30 March 2011Published online29 May 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The relationship between top-down enhancement and suppression of sensory cortical activity and large-scale neural networks remains unclear. Functional connectivity analysis of human functional magnetic resonance imaging data revealed that visual cortical areas that selectively process relevant information are functionally connected with the frontal-parietal network, whereas those that process irrelevant information are simultaneously coupled with the default network. This indicates that sensory cortical regions are differentially and dynamically coupled with distinct networks on the basis of task goals. View full text Figures at a glance * Figure 1: Experimental procedure. Participants were instructed to remember stimulus 1 (stim 1) and stimulus 2 (stim 2) and respond with either yes or no if the probe image matched either of the previous two relevant stimuli, as indicated by the task instructions. Participants maintained fixation on the white crosshairs throughout experiment. ISI, inter-stimulus interval; ITI, inter-trial interval. All participants provided informed, written consent in accordance with the Committee on Human Research oversight board at the University of California, San Francisco. * Figure 2: Network connectivity. (–) Connectivity maps associated with enhancement (SM-O > PV-O, ; FM-O > PV-O, ) and suppression (FM-O > PV-O, ; SM-O > PV-O, ) and contrast maps between suppression and enhancement networks (,) for both PPA (,,) and FFA (,,). Whole-brain maps were cluster-corrected for multiple comparisons at P = 0.05 and displayed at P < 0.01. Labeled regions are as follows: 1, right middle frontal gyrus; 2, left inferior frontal junction; 3, right inferior frontal junction; 4, mPFC; 5, PCC. Author information * Author information * Supplementary information Affiliations * Department of Neurology, Physiology and Psychiatry, W.M. Keck Foundation Center for Integrative Neuroscience, University of California, San Francisco, USA. * James Z Chadick & * Adam Gazzaley Contributions J.Z.C. and A.G. designed the experiment. J.Z.C. collected and analyzed data. J.Z.C. and A.G. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Adam Gazzaley Author Details * James Z Chadick Search for this author in: * NPG journals * PubMed * Google Scholar * Adam Gazzaley Contact Adam Gazzaley Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–8, Supplementary Tables 1–3, Supplementary Discussion, Supplementary Methods and Supplementary Results Additional data - A readily retrievable pool of synaptic vesicles
- Nat Neurosci 14(7):833-839 (2011)
Nature Neuroscience | Article A readily retrievable pool of synaptic vesicles * Yunfeng Hua1, 2, 7 * Raunak Sinha1, 2, 7 * Cora S Thiel1, 2, 7 * Roman Schmidt3 * Jana Hüve2, 4 * Henrik Martens5 * Stefan W Hell3 * Alexander Egner3, 6 * Jurgen Klingauf1, 2, 4 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:833–839Year published:(2011)DOI:doi:10.1038/nn.2838Received15 November 2010Accepted14 April 2011Published online12 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Although clathrin-mediated endocytosis is thought to be the predominant mechanism of synaptic vesicle recycling, it seems to be too slow for fast recycling. Therefore, it was suggested that a presorted and preassembled pool of synaptic vesicle proteins on the presynaptic membrane might support a first wave of fast clathrin-mediated endocytosis. In this study we monitored the temporal dynamics of such a 'readily retrievable pool' of synaptic vesicle proteins in rat hippocampal neurons using a new type of probe. By applying cypHer5E, a new cyanine dye–based pH-sensitive exogenous marker, coupled to antibodies to luminal domains of synaptic vesicle proteins, we could reliably monitor synaptic vesicle recycling and demonstrate the preferential recruitment of a surface pool of synaptic vesicle proteins upon stimulated endocytosis. By using fluorescence nanoscopy of surface-labeled synaptotagmin 1, we could resolve the spatial distribution of the surface pool at the periactive z! one in hippocampal boutons, which represent putative sites of endocytosis. View full text Figures at a glance * Figure 1: Antibodies coupled to cypHer are a reliable tool to measure stimulation-dependent exo-endocytic cycling of endogenous vesicle constituents. () Average normalized (norm.) fluorescence of anti-Syt1–cypHer-stained hippocampal boutons from multiple trials (each trial consisted of >30 boutons) as a function of pH. The solid curve is a fit to a Henderson–Hasselbalch equation (adjusted R2 = 0.99), yielding a pKa of 7.05. Error bars represent s.d. () Schematic depicting fluorescence changes of cypHer during exo-endocytosis. The red dots show cypHer-coupled antibodies, which bind to the extracellular domain of vesicular proteins (Syt1 or VGAT) and remain quenched owing to the neutral extracellular pH. During endocytosis and reacidification, however, the cypHer fluorescence is dequenched. Re-exocytosis upon stimulation results in quenching of the cypHer fluorescence at the extracellular neutral pH. () Fluorescence image of hippocampal neurons labeled with anti-VGAT–cypHer. The label forms a punctate pattern with puncta typically representing individual boutons. () Fluorescence images of hippocampal neurons stained w! ith anti-VGAT–cypHer before and directly after application of a stimulus (stim.) of 900 action potentials (APs) and 100 s later. () Average normalized fluorescence transient of anti-Syt1–cypHer-stained boutons in response to different action potential trains at 20 Hz (n = 3 experiments with >50 boutons for each). Traces were corrected for photobleaching. Error bars represent s.e.m. () Average normalized fluorescence transient of anti-VGAT–cypHer-stained boutons in response to different action potential trains at 20 Hz (n = 3 experiments with >50 boutons for each). Traces were corrected for photobleaching. Error bars represent s.e.m. * Figure 2: Dose–response curve to analyze the size of the surface pool. () Averaged fluorescence transients of anti-Syt1–cypHer-stained boutons in response to 50, 100 and 200 action potentials at 20 Hz (n = 5 experiments with >50 boutons for each). Lines were fitted to the initial fluorescence recovery phases to estimate the endocytic rate constant during stimulation. Back-extrapolation to zero yields the exocytosis amplitudes. Error bars represent s.e.m. () Average peak changes in normalized (norm.) fluorescence (ΔF) plotted as a function of action potential number (filled circles) together with ΔF values corrected for endocytosis during stimulation (triangles) by back-extrapolation (). These data points lie close to a line fit. () Average fluorescence response to an acid pulse of boutons labeled with anti-Syt1–cypHer, yielding the size of the surface pool. Error bars represent s.e.m. () Distribution of bouton fluorescence responses to an acid pulse. The solid black line is a Gaussian fit (adjusted R2 = 0.99) yielding a mean size of 49.46! ± 1.56 a.u. Inset, mean size of the surface pool estimated by the acid pulse is equivalent to the absolute fluorescence change triggered by 70 action potentials (APs). * Figure 3: Comparison of vesicle recycling kinetics probed with synaptopHluorin and cypHer-coupled antibodies. () Top: staining of spH-transfected hippocampal neurons with anti-Syt1–cypHer. Fluorescence images show colocalization of individual boutons expressing spH (green) with endogenous Syt1 (red) labeled with anti-Syt1–cypHer. Bottom: average fluorescence transients of double-labeled boutons in response to 200 action potentials at 20 Hz (n = 5 experiments with >50 boutons for each). Traces were corrected for photobleaching. Error bars represent s.e.m. () Top: staining of spH-transfected hippocampal neurons with anti-VGAT–cypHer. Fluorescence images show colocalization of individual boutons expressing spH (green) with the inhibitory synapse marker VGAT (red). Bottom: average fluorescence transients of double-labeled boutons in response to 200 action potentials (APs) at 20 Hz (n = 5 experiments with >50 boutons for each). Traces were corrected for photobleaching. Error bars represent s.e.m. Scale bars represent 5 μm. * Figure 4: Readily retrievable surface pool of synaptic vesicle constituents. () Cleaving membrane-stranded spH with TEV protease does not affect the cypHer signal at anti-Syt1–cypHer co-stained boutons. Average normalized (norm.) fluorescence responses to 50 action potentials at 20 Hz (n = 3 experiments with >50 boutons for each). The spH fluorescence (black) shows little or no recovery, whereas the cypHer signal (gray) shows normal fluorescence recovery, demonstrating that the Syt1 molecules that were endocytosed constitute a different population from those freshly exocytosed. Error bars represent s.e.m. (,) Photobleaching for 5 min at external pH 8.5 preferentially affects the vesicular pool of cypHer-tagged molecules. Fifty action potentials at 20 Hz induced typical spH fluorescence transients (black, n = 5 experiments with >50 boutons for each). However, the cypHer signal (gray) for both Syt1 () and VGAT () increases in parallel to the spH decay, indicating endocytosis of vesicle proteins from an RRetP resident on the bouton membrane. Error bar! s represent s.e.m. (,) Average fluorescence profiles of boutons (n = 3 experiments with >50 boutons for each) labeled with only anti-Syt1–cypHer () or anti-VGAT–cypHer (). Experiments were performed as in panels and , but using non-transfected neuronal cultures. This indicates that spH overexpression does not alter the dynamics or size of the RRetP. Error bars represent s.e.m. () Comparison of RRP and RRetP sizes measured by different labeling strategies for Syt1 and VGAT. 'Prebleach' denotes the cypHer fluorescence decrease upon 50 action potentials at 20 Hz, a measure of the RRP size (40.2 ± 6.9 a.u. for Syt1 and 34.8 ± 3. 6 a.u. for VGAT). 'Postbleach' denotes the fluorescence increase (19.5 ± 6.2 a.u. for Syt1 and 17.7 ± 2.1 a.u. for VGAT) subsequent to vesicular cypHer bleaching upon 50 action potentials at 20 Hz (as shown in and ). 'Surface staining' denotes the fluorescence increase upon 50 action potentials at 20 Hz (53. 7 ± 4.8 a.u.) after preferential lab! eling of the Syt1 surface pool (see Fig. 5a). Error bars repre! sent s.e.m. * Figure 5: Repeated stimulation reveals reuse of the RRetP. Average fluorescence profile of surface Syt1-labeled boutons (n = 3 experiments with >50 boutons for each) stimulated first with 50 action potentials at 20 Hz followed by a second stimulation with either 50 (black) or 200 (gray) action potentials at 20 Hz (gray). In response to the second stimulus, a marked fraction of synaptic vesicles recycled from the RRetP upon the first stimulus is released. Error bars represent s.e.m. * Figure 6: Spatial organization of the RRetP. Three-dimensional dual-color isoSTED nanoscopy of cultured hippocampal boutons showing the localization of surface-stranded Syt1 (red) at synapses identified by the pre- and postsynaptic markers RIM1 and RIM2 (RIM) and Homer1 (green), supported by 4Pi microscopy data of a large number of synapses. () z-projection of a three-dimensional image stack of Syt1 at synapses labeled by the CAZ markers RIM1 and RIM2. () z-projection as in of Syt1 at synapses labeled by Homer1 as a PSD marker, with synapses at regions of interest numbered 1 to 3. Arrows mark the local orientation of the axon. () Perspective views of volume-rendered data. The Syt1 labeling reveals preassembled patches that are localized in the periphery of the active zone, typically within 500 nm distance from the postsynaptic marker. () Spatial distribution of Syt1 with respect to Homer1 for each region of interest. Histograms show Syt1–Homer1 cross-correlation, binned according to Syt1–Homer1 distance d and relat! ive azimuth angle α, which was measured with respect to the orientation of the axon as marked by arrows in . Distances between Syt1 patches and the postsynaptic marker accumulate at about 100–300 nm at angles where the probability of finding Syt1 patches that are not associated with Homer1 is reduced (α deviating from 0 and ± π). () Nearest neighbor distances between Syt1 and RIM1 and RIM2 (RIM) or Homer1 patches from dual-color 4Pi microscopy stacks of n = 74 (RIM1 and RIM2) and n = 41 (Homer1) synapses. Insets show examples of z-projections. Scale bars represent 1 μm. Rendering volumes: 4.5 μm × 7.0 μm × 0.4 μm for the full data set; 1.5 μm × 1.5 μm × 0.4 μm for regions of interest. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Yunfeng Hua, * Raunak Sinha & * Cora S Thiel Affiliations * Department of Membrane Biophysics, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany. * Yunfeng Hua, * Raunak Sinha, * Cora S Thiel & * Jurgen Klingauf * Department of Cellular Biophysics, Institute of Medical Physics and Biophysics, University of Muenster, Muenster, Germany. * Yunfeng Hua, * Raunak Sinha, * Cora S Thiel, * Jana Hüve & * Jurgen Klingauf * Department of NanoBiophotonics, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany. * Roman Schmidt, * Stefan W Hell & * Alexander Egner * Fluorescence Microscopy Facility Muenster, Center for Nanotechnology (CeNTech), Muenster, Germany. * Jana Hüve & * Jurgen Klingauf * Synaptic Systems GmbH, Goettingen, Germany. * Henrik Martens * Current address: Laser-Laboratorium Goettingen e.V., Goettingen, Germany. * Alexander Egner Contributions Y.H., R. Sinha and C.S.T. conducted the majority of the experiments. J.H. collected and analyzed the 4Pi microscopy data. IsoSTED microscopy and analysis was performed by R. Schmidt and A.E. in the department of S.W.H. H.M. synthesized the cypHer-conjugated antibodies. J.K. conceptualized the project and together with Y.H. and R. Sinha designed the experiments. R. Sinha and J.K. wrote the paper with the help of Y.H., C.S.T. and R. Schmidt. All authors discussed the results and commented on the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jurgen Klingauf Author Details * Yunfeng Hua Search for this author in: * NPG journals * PubMed * Google Scholar * Raunak Sinha Search for this author in: * NPG journals * PubMed * Google Scholar * Cora S Thiel Search for this author in: * NPG journals * PubMed * Google Scholar * Roman Schmidt Search for this author in: * NPG journals * PubMed * Google Scholar * Jana Hüve Search for this author in: * NPG journals * PubMed * Google Scholar * Henrik Martens Search for this author in: * NPG journals * PubMed * Google Scholar * Stefan W Hell Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Egner Search for this author in: * NPG journals * PubMed * Google Scholar * Jurgen Klingauf Contact Jurgen Klingauf Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (582K) Supplementary Figures 1–4 Additional data - KCNQ5 channels control resting properties and release probability of a synapse
- Nat Neurosci 14(7):840-847 (2011)
Nature Neuroscience | Article KCNQ5 channels control resting properties and release probability of a synapse * Hai Huang1, 2 * Laurence O Trussell1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:840–847Year published:(2011)DOI:doi:10.1038/nn.2830Received13 January 2011Accepted07 April 2011Published online12 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Little is known about which ion channels determine the resting electrical properties of presynaptic membranes. In recordings made from the rat calyx of Held, a giant mammalian terminal, we found resting potential to be controlled by KCNQ (Kv7) K+ channels, most probably KCNQ5 (Kv7.5) homomers. Unlike most KCNQ channels, which are activated only by depolarizing stimuli, the presynaptic channels began to activate just below the resting potential. As a result, blockers and activators of KCNQ5 depolarized or hyperpolarized nerve terminals, respectively, markedly altering resting conductance. Moreover, the background conductance set by KCNQ5 channels, together with Na+ and hyperpolarization-activated and cyclic nucleotide–gated (HCN) channels, determined the size and time course of the response to subthreshold stimuli. Signaling pathways known to directly affect exocytic machinery also regulated KCNQ5 channels, and increase or decrease of KCNQ5 channel activity controlled relea! se probability through alterations in resting potential. Thus, ion channel determinants of presynaptic resting potential also control synaptic strength. View full text Figures at a glance * Figure 1: Identification of presynaptic KCNQ current. () A slow voltage ramp (13 mV s−1) with a K+-based internal solution evoked an outward current (black) that was strongly inhibited by 10–20 μM XE991, a KCNQ channel blocker (gray). () Outward current blocked by the KCNQ channel blocker linopirdine. () Outward current potentiated by the KCNQ channel opener flupirtine. () Outward current potentiated by retigabine. All traces were recorded in the presence of CdCl2, tetrodotoxin, CsCl, 4-AP and TEA-Cl to block Ca2+, Na+, HCN, and Kv1 and Kv3 channels (see Online Methods). * Figure 2: Voltage dependence of presynaptic KCNQ current. () A slow voltage ramp (8 mV s−1) evoked an outward current (black) that was partially blocked by 20 μM XE991 (gray). (,) XE991-sensitive current, obtained by subtracting the gray trace in panel from the black. Traces are shown over the full range () and with the initial portion of the membrane potential scale expanded (). Activation KCNQ current is apparent at approximately −85 mV. () Conductance–voltage curve of the KCNQ current. Gray, Boltzmann fit, with parameters as indicated. Gmax, maximal conductance. () Current–voltage relation in the presence of Cd2+ and tetrodotoxin. Black, control; dark gray, 10 μM XE991; light gray, subtraction of XE991 curve from control curve. () Conductance–voltage curve of the KCNQ current from . Gray, Boltzmann fit. () A depolarizing pulse from −80 mV to −40 mV evoked an outward current recorded in the absence of channel blockers. The outward current (black) was largely suppressed by XE991, leaving a smaller outward current w! ith a fast-inactivating component (gray). () XE991-sensitive current, obtained by subtracting the traces in panel . Current activation was fitted by an exponential function (gray) with fast and slow components of 35 ms (67%) and 852 ms and a weighted time constant of 308 ms. * Figure 3: KCNQ5 is expressed in the calyx of Held. (–) Absence of labeling for KCNQ2, KCNQ3 and KCNQ4, respectively. () Strong labeling for KCNQ5. Confocal settings were identical for panels –. Scale bar for –, 20 μm. (,) Labeling for KCNQ5 () in a slice in which a calyx had been recorded and filled with biocytin (). Scale bar for –, 5 μm. () Overlay of biocytin and KCNQ5 label, showing expression of the channel in the calyx of Held. (,) Labeling for KCNQ5 () in a slice in which a postsynaptic cell had been filled with biocytin (). () Overlay of and shows no postsynaptic somatic expression of KCNQ5. () KCNQ current inhibited by diclofenac. () KCNQ current potentiated by UCL2077. Traces in , were recorded in the presence of CdCl2, tetrodotoxin, CsCl, 4-AP and TEA-Cl to block Ca2+, Na+, HCN and Kv1 and Kv3 channels (see Online Methods). * Figure 4: Effects of KCNQ channels on resting membrane properties of calyces. () Bath application of 10 μM XE991 depolarized RMP. () Bath application of 20 μM flupirtine (Flup) hyperpolarized the RMP. (,) XE991 (10–20 μM) depolarized the membrane and decreased resting conductance (n = 7). (,) Flupirtine (20 μM) hyperpolarized the calyx and increased resting conductance (n = 4). Conductance was estimated in current clamp with small positive and negative current steps around the RMP. () In the presence of 10 μM XE991, 10 nM margatoxin (Marg) strongly depolarized the membrane. () Margatoxin alone had no significant effect on the RMP. () Mean voltage changes produced by margatoxin, XE991 (XE), or margatoxin with XE991. Voltage change that was induced by margatoxin alone was not significantly different from zero (P = 0.23, n = 4). Voltage changes induced by XE991 and by XE991 with margatoxin were significantly different from zero and were significantly different from one another. *P < 0.05; **P < 0.01; ***P < 0.001; error bars, ± s.e.m. * Figure 5: KCNQ channels determine properties of subthreshold stimuli. (,) Puff application of isoguvacine (Isog) evoked depolarized responses (black), whose amplitude (ampl) was potentiated and whose decay was slowed by 10 μM XE991 (gray). () Under voltage clamp, isoguvacine-induced current was not affected by XE991. (–) Voltage responses to synaptic-like waveforms (rise time constant, 7.5 ms; decay time constant, 25 ms; top trace in ) of different amplitudes injected into the calyx. Black, voltage responses; gray, responses recorded with 10 μM XE991. Voltage response traces are averages of 4–8 applications. (,) Statistical data summarizing XE991 effects on response amplitudes and Vhalf values in –. *P < 0.05; **P < 0.01; error bars, ± s.e.m. * Figure 6: Modulation of KCNQ current by PIP2 and protein kinase C. (,) A slow voltage ramp (13 mV s−1; ) or a voltage step from −80 mV to −30 mV () evoked an outward KCNQ current (black) that was partially inhibited by PI4K inhibitor PAO (50 μM; gray). () PAO positively shifted the conductance–voltage curve. G, conductance; Gmax, maximal conductance. (,) Phorbol 12-myristate 13-acetate (PMA; 2 μM), a potent PKC activator, inhibited the outward KCNQ current evoked by a voltage ramp () or a voltage step (). The PMA effects were blocked by PKC inhibitory peptide PKC19–31 (inset in ). The voltage ramp in the inset was from −100 to +20 mV, as in main panel. () PMA had no effects on the conductance–voltage relationship. All traces were recorded in the presence of CdCl2, tetrodotoxin, CsCl and 4-AP to block Ca2+, Na+, HCN and Kv1 and Kv3 channels. Boltzmann fit parameters given in text. Error bars, ± s.e.m. * Figure 7: Regulation of transmitter release by KCNQ channels. (,) Application of 10 μM XE991 increased the first EPSC amplitude but has a smaller effect on the second EPSC in a paired-pulse protocol. () XE991 significantly decreased the paired-pulse ratio. () Flupirtine (10 μM) decreased the first EPSC amplitude (ampl) but had a smaller effect on the second EPSC. () XE991 (10 μM) increased all EPSCs but to different degrees. The calyx was first preconditioned by 20-Hz stimuli for 10 s, followed immediately by a period of 100-Hz stimuli. Each trace is an average of 4–10 recordings. *P < 0.05; ***P < 0.001; error bars, ± s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Oregon Hearing Research Center, Oregon Health and Science University, Portland, Oregon, USA. * Hai Huang & * Laurence O Trussell * Vollum Institute, Oregon Health and Science University, Portland, Oregon, USA. * Hai Huang & * Laurence O Trussell Contributions H.H. conducted and analyzed all experiments. H.H. and L.O.T. designed experiments and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Hai Huang Author Details * Hai Huang Contact Hai Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Laurence O Trussell Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (422K) Supplementary Figures 1–6 Additional data - Rapid activity-induced transcription of Arc and other IEGs relies on poised RNA polymerase II
- Nat Neurosci 14(7):848-856 (2011)
Nature Neuroscience | Article Rapid activity-induced transcription of Arc and other IEGs relies on poised RNA polymerase II * Ramendra N Saha1 * Erin M Wissink1, 4 * Emma R Bailey1, 4 * Meilan Zhao1 * David C Fargo2 * Ji-Yeon Hwang1 * Kelly R Daigle1 * J Daniel Fenn1 * Karen Adelman3 * Serena M Dudek1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:848–856Year published:(2011)DOI:doi:10.1038/nn.2839Received14 February 2011Accepted07 April 2011Published online29 May 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Transcription of immediate early genes (IEGs) in neurons is highly sensitive to neuronal activity, but the mechanism underlying these early transcription events is largely unknown. We found that several IEGs, such as Arc (also known as Arg3.1), are poised for near-instantaneous transcription by the stalling of RNA polymerase II (Pol II) just downstream of the transcription start site in rat neurons. Depletion through RNA interference of negative elongation factor, a mediator of Pol II stalling, reduced the Pol II occupancy of the Arc promoter and compromised the rapid induction of Arc and other IEGs. In contrast, reduction of Pol II stalling did not prevent transcription of IEGs that were expressed later and largely lacked promoter-proximal Pol II stalling. Together, our data strongly indicate that the rapid induction of neuronal IEGs requires poised Pol II and suggest a role for this mechanism in a wide variety of transcription-dependent processes, including learning and me! mory. View full text Figures at a glance * Figure 1: TTX withdrawal effectively induces rapid Arc transcription. () Recordings were performed from neurons on multi-electrode arrays in the presence of TTX (48 h) or 1–2 min after TTX washout. Representative traces from different electrodes are shown for these two conditions. Note the presence of action potentials after TTX washout. () Representative confocal images of neurons treated with TTX for 48 h (TTX), washed with medium and fixed after 5 min (W5). These were then subjected to FISH with fluorescein-labeled riboprobes to Arc, which were detected with a Cy3-based fluorescent substrate. We detected Arc foci in 48 out of 101 nuclei (W5) and 5 out of 159 nuclei (TTX) from three independent biological trials. Scale bar represents 5 μm. () Graphical representation of the level of Arc pre-mRNA and mRNA at different time points after TTX washout, as detected by quantitative PCR (qPCR) and normalized to Gapdh (n = 3 biological trials). Error bars represent s.e.m. *P < 0.05 and **P < 0.01. () Representative fluorescent images of dissociate! d neurons immunostained with antibody to the neuronal marker NeuN (detected with Alexa488-conjugated antibody to mouse) and antibody to Arc (detected with Alexa633-conjugated antibody to rabbit). Neurons were treated with TTX for 48 h (TTX) and then washed with medium and fixed after 1 h (TTX washout) (n = 3 biological trials). The average Alexa633 fluorophore intensities (representing Arc) were estimated as follows: TTX, 42.91 ± 7.27, n = 38 neurons; TTX washout, 104.53 ± 15.98, n = 46 neurons. Scale bar represents 20 μm. * Figure 2: RNA Pol II is enriched at the Arc TSS. () Graphical map (not to scale) to show relative position of primers used to quantify immunoprecipitated chromatin. Primer information is provided in Supplementary Table 2. () Quantification of RNA Pol II binding to promoter regions and gene bodies of Arc as determined by ChIP with three antibodies to RNA Pol II: M01 and 8WG16 (specific for Rpb1) and an antibody to Rpb1 pSer5 CTD (n = 3 biological trials). Error bars represent s.d. *P < 0.01. Note the enrichment near the Arc TSS. Inset, pictorial representation of Pol II antibodies and epitopes that we used. () ChIP-seq signals, scaled equally on the y axis, are shown for Arc, Gfap and Scn2a1. Arrow indicates TSS for each gene and the accompanying number in Arc and Scn2a1 refers to the Pol II stalling indices (SI) for each gene (refer to the text). Pol II is unbound in Gfap. * Figure 3: Activity promotes Pol II escape from stalling into productive elongation. () Quantification of RNA Pol II in the Arc TSS as determined by ChIP with 8WG16 antibody at the indicated time points after TTX withdrawal (n = 3 biological trials). Error bars represent s.d. *P < 0.05 and **P < 0.01. () Quantification of RNA Pol II in the Arc gene body as determined by ChIP with M01 antibody at the indicated time points after TTX withdrawal. Data presented as the percentage Pol II level at the Arc TSS using primer pair 4 in the presence of TTX (100%) (n = 3 biological trials). Error bars represent s.d. () TTX washout elevated pSer2 Pol II (elongation competent) levels in Arc exon 3, as estimated by ChIP with antibody to pSer2 (H5) 5 min after TTX withdrawal. This enhancement was blocked by flavopiridol, an inhibitor of the kinase responsible for this modification. Exon 3–specific primers (primer pair 9; see map in ) were used because RNA Pol II is predominantly phosphorylated at this epitope as it approaches termination near the 3′ end of genes43 (n = 3! biological trials). Error bars represent s.d. * Figure 4: Arc Pol II stalling and NELF. (,) NELF enrichment near the Arc TSS was estimated by ChIP with antibodies to NELF-A () and NELF-E (). Error bars represent s.d. () RT-PCR data for Nelf-a and Nelf-e mRNA levels in uninfected neurons and neurons expressing a scrambled sequence (sc-shRNA) or Nelf-a or Nelf-e antisense shRNA (Nelf-a shRNA or Nelf-e shRNA). Error bars represent s.e.m. (,) NELF-A or NELF-E levels in neurons expressing a scrambled sequence or a shRNA against the NELF subunit. Blots were immunoprobed with antibody to actin as a loading control. Full length-blots are shown in Supplementary Figure 8. () Quantification of tri-methylation of histone H3 at lysine 4 (H3K4.3me) in Arc by ChIP with antibody to H3K4.3me in control neurons or neurons with depleted levels of the indicated NELF subunit. Error bars represent s.d. () Quantification of RNA Pol II in the Arc gene body as determined by ChIP with antibody to Rpb1 pSer5 CTD at W5 in neurons expressing the indicated shRNAs. Error bars represent s.d. ! () Graphical representation of Arc pre-mRNA levels in neurons expressing scrambled, Nelf-a shRNA or Nelf-e shRNA and treated as in (n = 5 biological trials). Error bars represent s.e.m. () Plotted are levels of Arc mRNA in neurons treated as in , but collected either 15 (n = 6) or 45 min (n = 3) after TTX withdrawal. Error bars represent s.e.m. For all, n = 3 biological trials if not mentioned otherwise. *P < 0.05, **P < 0.01. * Figure 5: Identifying other IEGs. () Heat map showing relative mRNA expression of all genes 15 or 45 min after TTX withdrawal (derived from microarray data). () Heat map showing relative pre-mRNA expression of all genes 5 min after TTX withdrawal (derived from one-step qRT-PCR data). In both and , more than twofold induction is represented in shades of blue, whereas less than twofold induction is shown in shades of red. Genes with twofold or more induction of mRNA at 15 min or pre-mRNA transcript at 5 min were classified as rapid IEGs. () Schematic flowchart representing multiple techniques used to validate rapid IEGs and delayed IEGs. * Figure 6: Pol II is enriched near all rapid IEG promoters. () Pol II bound promoters were assessed from M01 ChIP-seq for all rat genes, 21 rapid IEGs and 27 delayed IEGs. The percentage of genes with greater Pol II binding than intergenic regions (noise) are represented (n = 2 biological trials). () Box plot representation of the promoter proximal stalling index distribution for all rat genes (n = 7,324), rapid IEGs (n = 20) and delayed IEGs (n = 5) with Pol II bound promoters. The box and whiskers denote the 25–75th and the 5–95th percentiles, respectively. The median (all genes, 4.71; rapid IEGs, 7.63; delayed IEGs, 3.72) is represented by the horizontal bar. *P = 0.0175 (Kruskal-Wallis nonparametric test). () Examples of Pol II bound and unbound promoters with or without stalling. Arrow indicates TSS for each gene and the number in parentheses refers to stalling indices for Pol II bound promoters (refer to the text). () NELF-E enrichment in the promoter regions of rapid IEGs (blue) and delayed IEGs (red). For all genes, prime! r pair 1 represents upstream regions, primer pair 2 spans the TSS, and primer pairs 3 and 4 represent the gene body (n = 3 biological trials). Error bars represent s.d. () For comparison, detection of Pol II enrichment in same genes as in (n = 3). Error bars represent s.d. *P < 0.01, **P < 0.001. * Figure 7: Pol II stalling mediates immediate transcription of several IEGs. () Pre-mRNA levels of various rapid IEGs and delayed IEGs 5 min after TTX washout in neurons infected with scrambled shRNA, Nelf-a shRNA or Nelf-e shRNA lentiviruses (n = 3 biological trials). Error bars represent s.e.m. (,) NELF-A depletion blocked immediate transcription of many IEGs. mRNA levels of different IEGs in neurons infected with Nelf-a shRNA or Sc-shRNA lentiviruses. Cells were collected either 15 () or 45 () min after TTX withdrawal. The qPCR data were normalized to Gapdh and are presented as the fold change of the corresponding mRNA level in neurons expressing Sc-shRNA with TTX (n = 3). Error bars represent s.e.m. *P < 0.05 and **P < 0.001. * Figure 8: Poised Pol II is found in vivo. () Pol II enrichment in the promoter regions of rapid IEGs (blue) and delayed IEGs (red) in brain samples. The M01 antibody was used. For all genes, primer pair 1 represents upstream regions, primer pair 2 spans the TSS, and primer pair 3 and 4 represents the gene body (n = 3 biological trials). Error bars represent s.d. () Detection of NELF-A enrichment in the promoter regions of rapid IEGs (blue) and delayed IEGs (red) using identical primers as in (n = 3). Error bars represent s.d. *P < 0.01, **P < 0.001. () Representative confocal images showing transcription of Arc in the cortex after a rat's exposure to a novel environment as revealed by FISH. Scale bar represents 20 μm. Images were acquired from the layer 2–3 of primary somatosensory area of the cortex (gray box in the cartoon) (n = 2). () Pre-mRNA levels of rapid and delayed IEGs after 5 min of exposure to a novel environment (n = 5 for Arc and Cox2, n = 3 for others). Error bars represent s.d. ***P < 0.05. y axis! is log2. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE22622 * GSE22878 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Erin M Wissink & * Emma R Bailey Affiliations * Laboratory of Neurobiology, National Institute of Environmental Health Sciences, US National Institutes of Health, Research Triangle Park, North Carolina, USA. * Ramendra N Saha, * Erin M Wissink, * Emma R Bailey, * Meilan Zhao, * Ji-Yeon Hwang, * Kelly R Daigle, * J Daniel Fenn & * Serena M Dudek * Library and Information Services, National Institute of Environmental Health Sciences, US National Institutes of Health, Research Triangle Park, North Carolina, USA. * David C Fargo * Laboratory of Molecular Carcinogenesis, National Institute of Environmental Health Sciences, US National Institutes of Health, Research Triangle Park, North Carolina, USA. * Karen Adelman Contributions R.N.S. and S.M.D. conceived and designed the study. Experiments were conducted by R.N.S., E.R.B., E.M.W., M.Z., J.-y.H., J.D.F. and K.R.D. Data analysis was performed by R.N.S., E.R.B., E.M.W. and M.Z. Bioinformatics analyses were performed by D.C.F. K.A. provided statistical analyses and technical and conceptual advice. R.N.S. and S.M.D. wrote the manuscript with input from E.M.W., E.R.B., D.C.F. and K.A. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Serena M Dudek Author Details * Ramendra N Saha Search for this author in: * NPG journals * PubMed * Google Scholar * Erin M Wissink Search for this author in: * NPG journals * PubMed * Google Scholar * Emma R Bailey Search for this author in: * NPG journals * PubMed * Google Scholar * Meilan Zhao Search for this author in: * NPG journals * PubMed * Google Scholar * David C Fargo Search for this author in: * NPG journals * PubMed * Google Scholar * Ji-Yeon Hwang Search for this author in: * NPG journals * PubMed * Google Scholar * Kelly R Daigle Search for this author in: * NPG journals * PubMed * Google Scholar * J Daniel Fenn Search for this author in: * NPG journals * PubMed * Google Scholar * Karen Adelman Search for this author in: * NPG journals * PubMed * Google Scholar * Serena M Dudek Contact Serena M Dudek Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (614K) Supplementary Figures 1–8 and Supplementary Tables 1 and 2 Additional data - TACE (ADAM17) inhibits Schwann cell myelination
- Nat Neurosci 14(7):857-865 (2011)
Nature Neuroscience | Article TACE (ADAM17) inhibits Schwann cell myelination * Rosa La Marca1, 2 * Federica Cerri1, 2 * Keisuke Horiuchi3 * Angela Bachi4 * M Laura Feltri4 * Lawrence Wrabetz4 * Carl P Blobel5 * Angelo Quattrini1, 2 * James L Salzer6, 7, 8 * Carla Taveggia1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:857–865Year published:(2011)DOI:doi:10.1038/nn.2849Received14 February 2011Accepted07 April 2011Published online12 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Tumor necrosis factor-α–converting enzyme (TACE; also known as ADAM17) is a proteolytic sheddase that is responsible for the cleavage of several membrane-bound molecules. We report that TACE cleaves neuregulin-1 (NRG1) type III in the epidermal growth factor domain, probably inactivating it (as assessed by deficient activation of the phosphatidylinositol-3-OH kinase pathway), and thereby negatively regulating peripheral nervous system (PNS) myelination. Lentivirus-mediated knockdown of TACE in vitro in dorsal root ganglia neurons accelerates the onset of myelination and results in hypermyelination. In agreement, motor neurons of conditional knockout mice lacking TACE specifically in these cells are significantly hypermyelinated, and small-caliber fibers are aberrantly myelinated. Further, reduced TACE activity rescues hypomyelination in NRG1 type III haploinsufficient mice in vivo. We also show that the inhibitory effect of TACE is neuron-autonomous, as Schwann cells lack! ing TACE elaborate myelin of normal thickness. Thus, TACE is a modulator of NRG1 type III activity and is a negative regulator of myelination in the PNS. View full text Figures at a glance * Figure 1: TACE downregulation induces precocious myelination and hypermyelination in vitro. () Western blot of rat Schwann cells that were uninfected (WT) or infected with lentiviruses expressing one of three different shRNAs specific for Tace (sh1, sh2 and sh3) or a scrambled artificial sequence (shscr). Amounts of TACE and of actin, as a loading control, were determined 7 d after infection. TACE expression was significantly lower in Schwann cells infected with sh1, sh2 or sh3, but not in shscr-infected or uninfected samples. Full-length blots are shown in Supplementary Figure 10. () TACE expression in Schwann cells infected with sh1, sh2 or sh3 **P = 0.0019 (WT versus sh1), **P = 0.0025 (WT versus sh2), **P = 0.0018 (WT versus sh3). Data shown are the averages of three experiments; error bars, mean ± s.e.m. () Quantification of MBP-positive segments 3 d after the induction of myelination in control cultures and cultures infected with Tace shRNA or shscr. Quantification was performed on the entire culture (a total of three coverslips per experiment) **P = 0.0012 ! (WT versus sh1), **P = 0.0047 (WT versus sh2), ***P < 0.0001 (WT versus sh3). Data shown are averages of three experiments; error bars, mean ± s.e.m. () Western blot of organotypic rat Schwann cell neuronal cocultures that were uninfected or infected with lentiviruses expressing sh1, sh2 or sh3, or shscr. Lysates were tested for MPZ, and for actin as a loading control, 14 d after the induction of myelination. MPZ expression was significantly upregulated in cultures in which TACE was knocked down. () Organotypic rat Schwann cell neuronal cocultures infected with lentiviruses expressing TACE-specific shRNAs or shscr were maintained in myelinating conditions for 3 d, fixed and stained for MBP (rhodamine) and neurofilament (fluorescein). Numerous myelin segments were evident in Tace shRNA–infected cultures; none formed in shscr-infected cultures and only a few formed in uninfected cultures. Scale bar, 100 μm. * Figure 2: In vitro hypermyelination is neuron-autonomous. () Cocultures of mouse neurons infected with Tace-specific shRNA (sh1) or a scrambled artificial sequence shRNA (shscr), rid of endogenous Schwann cells and repopulated with wild-type, uninfected rat Schwann cells, were maintained in myelinating conditions for 14 d and then stained for MBP (rhodamine) and neurofilament (fluorescein). Numerous myelin segments are evident in sh1-infected cultures and fewer in uninfected (WT) and shscr-infected cultures. Scale bar, 100 μm. () Quantification of MBP+ segments 14 d after the induction of myelination in control and infected (Tace shRNA or shscr) neuronal cocultures. Quantification was performed on the entire culture (three coverslips per experiment). ***P = 0.0002 (WT versus sh1), ***P = 0.0007 (WT versus sh2), ***P = 0.0009 (WT versus sh3). Data shown are averages of three experiments; error bars, mean ± s.e.m. () Cocultures of wild-type, uninfected mouse DRG neurons purified of endogenous Schwann cells and repopulated with rat ! Schwann cells that had previously been infected with sh1 or shscr. Cultures were maintained in myelinating conditions for 21 d, and then stained for MBP (rhodamine) and neurofilament (fluorescein). No difference in myelination was observed in infected versus control cultures. Scale bar, 100 μm. () Quantification of MBP+ segments 21 d after the induction of myelination in control and Schwann cells infected with Tace shRNA or shscr and cocultured with DRG neurons. Quantification was performed on the entire culture (three coverslips per experiment, for three experiments; P was not significant). * Figure 3: TACE inactivation in motor neurons leads to precocious myelination. () RT-PCR analysis of Tace mRNA expression in wild-type (WT) and HB9-cre; Tacefl/fl (KO) spinal cords. Schwann cell mRNA (SC) was used as a positive control. Gapdh expression was a control for amplification. () Lysates of wild-type and HB9-cre; Tacefl/fl P30 femoral nerves were fractionated by SDS-PAGE and blotted with antibodies to TACE and to actin as a loading control. TACE expression was lower in HB9-cre; Tacefl/fl femoral nerve extracts than in wild-type extracts. () Electron micrographs of P1 ventral roots from wild-type and HB9-cre; Tacefl/fl mice. In HB9-cre; Tacefl/fl mice, fibers are hypermyelinated as compared with controls. Scale bars, 2 μm. () Electron micrographs of P1 sciatic nerves from wild-type and HB9-cre; Tacefl/fl mice; most fibers in HB9-cre; Tacefl/fl mice were sorted, and many were already myelinated. Scale bars, 2 μm. * Figure 4: HB9-cre; Tacefl/fl mice are hypermyelinated during development. () Semithin sections (top panels) and electron micrographs (bottom panels) of wild-type and HB9-cre; Tacefl/fl P7 nerves. Scale bars, 15 μm (top panels) and 2 μm (bottom panels). () Semithin sections (top panels) and electron micrographs (bottom panels) of wild-type and HB9-cre; Tacefl/fl P15 nerves. Scale bars, 20 μm (top panels) and 2 μm (bottom panels). () g ratios as a function of axon diameter were significantly different between wild-type (WT) P7 mice and HB9-cre; Tacefl/fl (KO) P7 mice (P = 0.0007). The graph represents the g ratios obtained from more than 500 myelinated axons per genotype (a total of three mice per genotype). () The distribution of myelinated fibers was similar in HB9-cre; Tacefl/fl and wild-type P7 sciatic nerves (P was not significant). () Myelinated axons of similar diameters demonstrate that myelin in wild-type versus HB9-cre; Tacefl/fl sciatic nerves had identical periodicity but differed considerably in the number of lamellae. Scale bar, 50! 0 nm. * Figure 5: HB9-cre; Tacefl/fl adult mice were hypermyelinated and Remak fibers were aberrantly ensheathed. () Semithin sections (top panels) and electron micrographs (bottom panels) of wild-type and HB9-cre; Tacefl/fl P30 ventral roots. Scale bars, 20 μm (top panels) and 2 μm (bottom panels). Asterisks indicate heavily myelinated fibers of less than 1 μm diameter. () Semithin sections (top panels) and electron micrographs (bottom panels) of wild-type and HB9-cre; Tacefl/fl P30 sciatic nerves. Scale bars, 20 μm (top panels) and 2 μm (bottom panels). () Lysates of wild-type (WT) and HB9-cre; Tacefl/fl (KO) P15 and P30 femoral nerves were blotted with antibodies to MBP and to actin as a loading control. MBP expression was upregulated in femoral nerve extracts from HB9-cre; Tacefl/fl mice. () Lysates of wild-type and HB9-cre; Tacefl/fl P30 femoral nerves were blotted with antibodies to p-AKT, total AKT (tot-AKT) and actin as a loading control. p-AKT expression was notably upregulated in HB9-cre; Tacefl/fl femoral nerve extracts. () Impaired sorting of Remak fibers of HB9-cre; Ta! cefl/fl sciatic nerves. In wild-type mice, unmyelinated axons are segregated into separate pockets of Remak bundles and fully wrapped by Schwann cells. In HB9-cre; Tacefl/fl mice, Remak bundles contained abnormally large-caliber axons (asterisks), and Schwann cells failed to ensheath the axons. Scale bar, 1 μm. () Significant difference in g ratios as a function of axon diameter between P30 sciatic nerve fibers from wild-type and HB9-cre; Tacefl/fl mice (P < 0.0001). The graph represents the g ratio obtained from more than 700 myelinated axons (three mice per genotype). () Myelinated axons in HB9-cre; Tacefl/fl P30 sciatic nerves were significantly smaller (***P < 0.0001, **P = 0.0051). Approximately 7% of myelinated fibers were <1 μm in diameter in HB9-cre; Tacefl/fl sciatic nerves. In each genotype, we counted >700 axons in a total of three mice. Error bars, mean ± s.e.m. * Figure 6: Mpz-cre; Tacefl/fl mice were normally myelinated. () Genotyping PCR on genomic DNA prepared from sciatic nerves of P15 wild-type (+/+) and Mpz-cre; Tacefl/fl mice. Tace recombination was present in nerves of Tacefl/fl mice expressing the Cre recombinase also. Flx, amplification of the fl allele; Cre, amplification of cre; null, amplification of the recombined allele21. () Mpz-cre; Tacefl/fl mice had a comparable myelin thickness to wild-type mice. Semithin sections (top panels) and electron micrographs (bottom panels) of wild-type and Mpz-cre; Tacefl/fl P7 sciatic nerves. Scale bars, 20 μm (top panels) and 2 μm (bottom panels). () Semithin sections (top panels) and electron micrographs (bottom panels) of wild-type and Mpz-cre; Tacefl/fl P30 sciatic nerves. Scale bars, 20 μm (top panels) and 2 μm (bottom panels). () g ratios as a function of axon diameter were identical in wild-type (WT) and Mpz-cre; Tacefl/fl (KO) P30 sciatic nerve fibers (P was not significant). The graph represents the g ratios obtained from >300 myel! inated axons (three mice per genotype). () Lysates of wild-type and Mpz-cre; Tacefl/fl P15 sciatic nerves were fractionated by SDS-PAGE and blotted with antibodies to myelin proteins (MAG and MBP) and to actin as a loading control. No alteration in myelin protein expression was observed among wild-type and Mpz-cre; Tacefl/fl mice. () Mpz-cre; Tacefl/fl mice have a slightly but significantly greater axonal diameters than wild-type mice (*P < 0.036, **P < 0.001, ***P < 0.0001). Myelinated axons of P30 sciatic nerves were binned based on their axonal diameters. >1,200 axons were counted from three different mice per genotype. Error bars, mean ± s.e.m. * Figure 7: TACE cleaves NRG1 type III. () Lysates of wild-type, Tace+/− and Tace−/− DRG neurons were blotted with antibodies to NRG1 and to actin as loading control. The 135-kDa band corresponding to the full-length NRG1 pro-protein (arrowhead) was increased in Tace+/− and, to a greater extent, in Tace−/− lysates. The active cleaved fragment of NRG1 (arrow) was upregulated in Tace+/− and Tace−/− samples. () Lysates of wild-type (WT) and HB9-cre; Tacefl/fl (KO) P30 femoral nerves were blotted with antibodies to NRG1 and to actin as a loading control. Uncleaved NRG1 was present in wild-type but not in Tace−/− nerves (arrowhead), contrary to the cleaved, active NRG1 fragment, which is upregulated in HB9-cre; Tacefl/fl samples (arrow). () In vitro cleavage of the EGF domain of human recombinant NRG1 β1. NRG1 β1 was incubated with human recombinant TACE and then separated on an SDS-PAGE gel. The corresponding cleaved band (arrow) was digested with trypsin and analyzed by MALDI-TOF mass spectrom! etry. () MALDI-TOF spectrum of the 6.5 kDa band digested with endoproteinase Glu-C. Peaks with arrows are from NRG1, and unlabeled peaks are from autolysis mediated by endoproteinase Glu-C. () NRG1 β1 sequence representing the EGF domain (red), the β1 exon (yellow box) and the transmembrane domain (blue). TACE (α) and BACE1 (β) cleavage sites are also indicated. () Cultures of wild-type and Tace−/− DRG neurons were incubated with ERBB2-ERBB3–Fc and the binding visualized with rhodamine-conjugated antibodies to human Fc (rhodamine); neurofilament staining from corresponding fields is shown (fluorescein). Scale bar, 50 μm. () Quantification of the ERBB2-ERBB3 binding signal. Quantification was performed on confocal images that were acquired with the same z-stack and laser intensity (a total of five coverslips per experiment, for three experiments). ***P < 0.0001. Error bars, mean ± s.e.m. * Figure 8: TACE regulates NRG1 type III activity. () Cocultures of wild-type (WT) and Nrg1 type III−/– (KO) DRG neurons that were uninfected or infected with Tace-specific shRNA (sh1) or scrambled shRNA (shscr) were maintained in myelinating conditions with rat Schwann cells for 21 d and then stained for MBP (rhodamine) and neurofilament (fluorescein). Tace knockdown does not rescue myelination in Nrg1 type III−/– neurons. Scale bar, 50 μm. () Cocultures of wild-type or Nrg1 type III−/– DRG neurons were uninfected or infected with full-length NRG1 type III or NRG1 cleaved at the TACE cleavage site. These cocultures were maintained in myelinating conditions with wild-type rat Schwann cells for 14 d, fixed and stained for MBP (rhodamine) and neurofilament (fluorescein). NRG1 cleaved by TACE inefficiently rescued myelination in Nrg1 type III−/– neurons. The few MBP-positive segments observed (nine coverslips from three experiments) are shown. Scale bar, 50 μm. () Rat primary Schwann cells were starved for 16 ! h and not stimulated (control) or stimulated with 50 ng ml−1 NRG1 β1 (NRG1), 50 ng ml−1 NRG1 β1 cleaved by TACE (NRG1 + TACE) or 50 ng ml−1 TACE alone (TACE). Lysates of these cells were blotted with antibodies to p-AKT, total AKT (tot-AKT) and calnexin as a loading control. () Rat primary Schwann cells were starved for 16 h and not stimulated (control) or stimulated with 50 ng ml−1 NRG1 β1 (NRG1), 50 ng ml−1 NRG1 β1 cleaved by BACE1 (NRG1+BACE1) or 50 ng ml−1 BACE1 alone (BACE1). Lysates from these cells were blotted with antibodies to p-AKT, total AKT and calnexin as a loading control. () NRG1 cleaved by TACE was detected on the axonal surface of Nrg1 type III−/– neurons infected with lentiviruses expressing NRG1 processed at the TACE cleavage site by live staining for the NRG1 hemagglutinin (HA) epitope (rhodamine) and neurofilament (fluorescein). Scale bar, 50 μm. () Electron micrographs of NRG1 type III+/−, NRG1 type III+/−; Tace+/− and wild-! type P30 sciatic nerves. NRG1 type III+/−; Tace+/− fibers ! are normally myelinated. Scale bar, 2 μm. () g ratios of P30 sciatic nerve fibers as a function of axon diameter were lower in NRG1 type III+/−; Tace+/− mice than in NRG1 type III+/− mice (P < 0.0001). The graph represents the g ratios obtained from >200 myelinated axons (three mice per genotype). Author information * Abstract * Author information * Supplementary information Affiliations * Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy. * Rosa La Marca, * Federica Cerri, * Angelo Quattrini & * Carla Taveggia * Institute of Experimental Neurology, San Raffaele Scientific Institute, Milan, Italy. * Rosa La Marca, * Federica Cerri, * Angelo Quattrini & * Carla Taveggia * Department of Orthopedic Surgery, School of Medicine, Keio University, Tokyo, Japan. * Keisuke Horiuchi * Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milan, Italy. * Angela Bachi, * M Laura Feltri & * Lawrence Wrabetz * Arthritis and Tissue Degeneration Program, Hospital for Special Surgery at Weill Medical College of Cornell University, New York, New York, USA. * Carl P Blobel * Department of Cell Biology, New York University School of Medicine, New York, New York, USA. * James L Salzer * Department of Neurology, New York University School of Medicine, New York, New York, USA. * James L Salzer * Smilow Neuroscience Program, New York University School of Medicine, New York, New York, USA. * James L Salzer Contributions R.L.M. conducted most of the experiments. F.C. and A.Q. performed morphological and ultrastructural analyses of sciatic nerves and ventral roots. K.H., C.P.B., M.L.F. and L.W. provided transgenic lines and helped with discussions. A.B. performed the mass spectrometry analyses. J.L.S. provided support and initially contributed to the experimental design. C.T. designed the experimental plan, supervised the project and wrote the paper. All authors commented on the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Carla Taveggia Author Details * Rosa La Marca Search for this author in: * NPG journals * PubMed * Google Scholar * Federica Cerri Search for this author in: * NPG journals * PubMed * Google Scholar * Keisuke Horiuchi Search for this author in: * NPG journals * PubMed * Google Scholar * Angela Bachi Search for this author in: * NPG journals * PubMed * Google Scholar * M Laura Feltri Search for this author in: * NPG journals * PubMed * Google Scholar * Lawrence Wrabetz Search for this author in: * NPG journals * PubMed * Google Scholar * Carl P Blobel Search for this author in: * NPG journals * PubMed * Google Scholar * Angelo Quattrini Search for this author in: * NPG journals * PubMed * Google Scholar * James L Salzer Search for this author in: * NPG journals * PubMed * Google Scholar * Carla Taveggia Contact Carla Taveggia Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (7M) Supplementary Figures 1–10 and Supplementary Table 1 Additional data - Distinct functions of kainate receptors in the brain are determined by the auxiliary subunit Neto1
- Nat Neurosci 14(7):866-873 (2011)
Nature Neuroscience | Article Distinct functions of kainate receptors in the brain are determined by the auxiliary subunit Neto1 * Christoph Straub1, 2, 5 * David L Hunt3, 5 * Miwako Yamasaki4 * Kwang S Kim1, 2 * Masahiko Watanabe4 * Pablo E Castillo3 * Susumu Tomita1, 2 * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:866–873Year published:(2011)DOI:doi:10.1038/nn.2837Received14 March 2011Accepted14 April 2011Published online29 May 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Ionotropic glutamate receptors principally mediate fast excitatory transmission in the brain. Among the three classes of ionotropic glutamate receptors, kainate receptors (KARs) have a unique brain distribution, which has been historically defined by 3H-radiolabeled kainate binding. Compared with recombinant KARs expressed in heterologous cells, synaptic KARs exhibit characteristically slow rise-time and decay kinetics. However, the mechanisms responsible for these distinct KAR properties remain unclear. We found that both the high-affinity binding pattern in the mouse brain and the channel properties of native KARs are determined by the KAR auxiliary subunit Neto1. Through modulation of agonist binding affinity and off-kinetics of KARs, but not trafficking of KARs, Neto1 determined both the KAR high-affinity binding pattern and the distinctively slow kinetics of postsynaptic KARs. By regulating KAR excitatory postsynaptic current kinetics, Neto1 can control synaptic tempora! l summation, spike generation and fidelity. View full text Figures at a glance * Figure 1: Hippocampus-abundant Neto1 interacts with KARs in vivo. () Distinct expression of Neto1 and Neto2 in the brain. Neto1 was expressed strongly in hippocampus, whereas Neto2 was expressed in all brain regions except the hippocampus. All full and uncropped blots are shown in Supplementary Figure 8. () Neto1 and Neto2 co-immunoprecipitated with GluK2/3 and GluK5, but not with GluA1 or PSD-95, from rat brain lysate. Asterisk indicates Ig heavy chain. () Total protein levels of Neto1 and GluK5 were reduced in the hippocampus of GluK2 knockout (−/−) compared with heterozygous (+/−) mice, demonstrating a genetic interaction between GluK2 and Neto1 together with GluK5. Signal intensities were measured and normalized by those from Neto1 heterozygous (n = 4). () To compare protein expression at the PSD, we fractionated PSDs from hippocampi of the indicated genotypes (n = 4 each). Data are given as mean ± s.e.m. *P < 0.05, ***P < 0.005. * Figure 2: Neto 1 is highly expressed in the hippocampus CA3 pyramidal neurons and localizes at stratum lucidum. () Endogenous Neto1 promoter activity was monitored by β-galactosidase activity in the knockout, in which the Neto1 gene was replaced with the β-galactosidase gene. Strong β-galactosidase activity was observed in the hippocampus CA3 pyramidal cells, whereas weak activity was observed in the hippocampus CA1 interneurons, cerebral cortex and striatum. Boxes represent regions in which the recordings shown in Figure 4a–c were performed. () Localization of Neto1 protein shown by immunohistochemistry with an antibody to Neto1. Neto1 localized strongly at the hippocampus stratum lucidum, where mossy fiber and CA3 pyramidal cells form synapses, in the wild-type mouse (WT), but not in the Neto1 knockout mouse. DG, dentate gyrus. Scale bars represent 1 mm (left panels) and 200 μm (right panels). * Figure 3: Distinct distribution of high-affinity KARs is determined by Neto1 postsynaptically. () Kainate binding in coronal hippocampal sections were visualized using an autoradiographical technique with [3H]kainate (50 nM). A strong [3H]kainate signal was observed in the stratum lucidum (St.L.) in the wild type, but not in the GluK2 knockout. By contrast, the [3H]kainate signal was reduced in Neto1 knockout mice. Scale bar represents 100 μm. () Binding of various concentrations of [3H]kainate ([3H]KA) to hippocampal membranes was measured. The binding curve was shifted to the right in the Neto1 knockout. (,) Comparison of channel properties in transfected heterologous cells using a piezo-driven fast-perfusion system in outside-out patches. GluK2 and GluK5 were coexpressed with empty plasmid (mock) or Neto1 in tsA201 cells. Representative response to sustained 1 mM kainate application () and a 1-ms pulse (). Coexpression of Neto1 slowed desensitization and increased the ratio of steady-state and peak currents of GluK2/5 heteromeric channels (n = 7–12; ). Neto1 slo! wed deactivation of GluK2/5 (n = 7–9; ). Upper traces in and indicate the open-tip potential to confirm rate of solution exchange. Data are given as mean ± s.e.m. * Figure 4: Neto1 modulates KAR function in the hippocampus. (–) Representative examples of inward currents elicited by agonist (3 μM kainate) from Neto1 knockout and wild-type littermate mice recorded in the whole-cell voltage-clamp configuration in CA3 pyramidal cells (), CA1 striatum radiatum interneurons () and cerebellar Purkinje cells (). Kainate-evoked inward currents were recorded in the presence of 30 μM GYKI 53655, 50 μM D-AP5, 100 μM picrotoxin and 0.5 μM TTX. Each bar shows the mean peak current amplitude in each neuron from the numbers of cells;animals indicated. () To compare cell-surface expression of KARs, we prepared acute hippocampal slices and biotinylated them with cell-impermeable sulfo-NHS-SS-biotin. After solubilization, biotinylated proteins were precipitated with Neutravidin beads to isolate proteins at the cell surface. Most GluK2/3 was detected in the surface fraction, whereas a cytosolic protein, tubulin, was detected in the internal fraction. () No obvious change in surface expression of GluK2/3 or ! GluK5 was observed in acute hippocampal slices from wild-type and Neto1 knockout mice (n = 6). Scale bars represent 50 μm. Data are given as mean ± s.e.m. *P < 0.05, **P < 0.01. * Figure 5: The slow decay of KAR-mediated synaptic transmission is determined by Neto1. (,) Representative normalized KAR-mediated EPSCs showing faster EPSC decay time constant () and rise time () in Neto1 knockout mice compare with wild-type littermates. () Representative traces of AMPAR-mediated EPSCs (elicited by stimulation of associational/commissural fibers), and NMDAR-mediated EPSCs (elicited by mossy fiber stimulation) showing no difference between wild-type and Neto1 knockout littermates. () Representative example of KAR and NMDAR EPSCs. Mossy fiber–evoked mixed KAR and NMDAR responses were recorded at +30 mV in the presence of 30 μM GYKI 53655, 100 μM picrotoxin and 3 μM CGP 55845. After a baseline was acquired, 50 μM MK-801 was washed in to isolate pure KAR-EPSCs, which were subsequently abolished in the presence of 10 μM NBQX. Inset traces depict the residual KAR-EPSC following MK-801 wash-in for Neto1 knockout and wild-type littermates, and block by NBQX. () Representative AMPAR and NMDAR EPSCs from wild-type and Neto1 knockout mice. In all ! panels, black traces are from wild-type animals and gray traces are from Neto1−/− animals. * Figure 6: Localization of KARs in the brain is independent of Neto1 and its PDZ-binding domain. () GluK2/3 protein was observed by immunohistochemistry in stratum lucidum (St.L.), and no obvious difference was detected between the wild type (WT) and Neto1 knockout. Scale bars represent 200 μm. High-magnification confocal microscopy in stratum lucidum showed that GluK2/3 immunoreactivity (green) was unchanged in the Neto1 knockout, but was not detected in the GluK2 knockout. A presynaptic marker protein, synaptophysin (Sph, red), showed no difference among the three genotypes. Scale bars represent 20 μm. () Relative fluorescence intensity (GluK2/Sph) from randomly selected CA3 areas was measured (n = 6). () Biochemical fractionation of hippocampi showed enrichment of Neto1, Neto2, GluK2/3 and GluK5 in the PSD fraction together with PSD-95 and Shank1 as markers for the PSD. () Protein expression in the PSD fraction. No change in protein expression of ionotropic glutamate receptors and Neto2 was observed in the Neto1 knockout. For analysis of GluN2A and GluN2B, comparis! on between wild type and knockout littermates is shown (n = 4). Data are given as mean ± s.e.m. ***P < 0.005. * Figure 7: Neto1 modulates KAR-driven temporal summation and spike fidelity in CA3 pyramidal neurons. () Neto1 contributes to the charge transfer of KAR-EPSCs elicited by brief bursts (five pulses) of mossy fiber stimulation at 3, 10 and 30 Hz. Representative averaged traces are normalized to the peak of the first response. () Summary data indicate a significant attenuation of the KAR-EPCS charge transfer between wild-type and Neto1 knockout littermates. Normalized charge transfer was calculated by integrating the area under the curve for traces that were normalized to the peak amplitude of the first response. () Presynaptic facilitation was unchanged between wild-type and Neto1-knockout littermates. Pulse 5 / pulse 1 current amplitude ratios were calculated across the frequency range tested; no significant difference was observed. () Neto1 can affect spiking output of CA3 pyramidal cells driven by synaptic KARs. KAR-EPSPs were elicited by short bursts of mossy fiber stimulation (five pulses) while recording from CA3 pyramidal cells in whole-cell current clamp mode. Stimulat! ion intensity was adjusted such that a spike was elicited following the fourth stimulation 50% of the time. () Summary data of all experiments shown in (seven cells, three animals for wild type and knockout). () Even in the absence of glutamate receptor antagonists, spike probability during the fifth pulse was also significantly lower in Neto1 knockout animals than in their wild-type littermates (six cells, three animals for both genotypes). Data are given as mean ± s.e.m.; NS, not significant (P > 0.05); *P < 0.05. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Christoph Straub & * David L Hunt Affiliations * Program in Cellular Neuroscience, Neurodegeneration and Repair, Yale University School of Medicine, New Haven, Connecticut, USA. * Christoph Straub, * Kwang S Kim & * Susumu Tomita * Department of Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, Connecticut, USA. * Christoph Straub, * Kwang S Kim & * Susumu Tomita * Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, USA. * David L Hunt & * Pablo E Castillo * Department of Anatomy, Hokkaido University Graduate School of Medicine, Sapporo, Japan. * Miwako Yamasaki & * Masahiko Watanabe Contributions S.T. and P.E.C. conceived the project and wrote the manuscript. C.S., D.L.H., M.Y., K.S.K., M.W. and S.T. performed all of the experiments and analyzed results. All of the authors, C.S. and D.L.H. in particular, contributed to the final version of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Susumu Tomita or * Pablo E Castillo Author Details * Christoph Straub Search for this author in: * NPG journals * PubMed * Google Scholar * David L Hunt Search for this author in: * NPG journals * PubMed * Google Scholar * Miwako Yamasaki Search for this author in: * NPG journals * PubMed * Google Scholar * Kwang S Kim Search for this author in: * NPG journals * PubMed * Google Scholar * Masahiko Watanabe Search for this author in: * NPG journals * PubMed * Google Scholar * Pablo E Castillo Contact Pablo E Castillo Search for this author in: * NPG journals * PubMed * Google Scholar * Susumu Tomita Contact Susumu Tomita Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (14M) Supplementary Figures 1–8 Additional data - UNC119 is required for G protein trafficking in sensory neurons
- Nat Neurosci 14(7):874-880 (2011)
Nature Neuroscience | Article UNC119 is required for G protein trafficking in sensory neurons * Houbin Zhang1, 10 * Ryan Constantine1, 2, 10 * Sergey Vorobiev3 * Yang Chen3 * Jayaraman Seetharaman3 * Yuanpeng Janet Huang4 * Rong Xiao4 * Gaetano T Montelione4 * Cecilia D Gerstner1 * M Wayne Davis5 * George Inana6 * Frank G Whitby7 * Erik M Jorgensen5, 8 * Christopher P Hill7 * Liang Tong3 * Wolfgang Baehr1, 5, 9 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:874–880Year published:(2011)DOI:doi:10.1038/nn.2835Received28 February 2011Accepted26 November 2011Published online05 June 2011 Abstract * Abstract * Accession codes * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg UNC119 is widely expressed among vertebrates and other phyla. We found that UNC119 recognized the acylated N terminus of the rod photoreceptor transducin α (Tα) subunit and Caenorhabditis elegans G proteins ODR-3 and GPA-13. The crystal structure of human UNC119 at 1.95-Å resolution revealed an immunoglobulin-like β-sandwich fold. Pulldowns and isothermal titration calorimetry revealed a tight interaction between UNC119 and acylated Gα peptides. The structure of co-crystals of UNC119 with an acylated Tα N-terminal peptide at 2.0 Å revealed that the lipid chain is buried deeply into UNC119′s hydrophobic cavity. UNC119 bound Tα-GTP, inhibiting its GTPase activity, thereby providing a stable UNC119–Tα-GTP complex capable of diffusing from the inner segment back to the outer segment after light-induced translocation. UNC119 deletion in both mouse and C. elegans led to G protein mislocalization. Thus, UNC119 is a Gα subunit cofactor essential for G protein trafficki! ng in sensory cilia. View full text Figures at a glance * Figure 1: Crystal structure of human UNC119. () Ribbon representation of the structure of human UNC119 (residues 57–237). Nine β strands (β1–β9), shown in red, create two β sheets that splay apart at one end to create an opening to the cavity at the center of the β sandwich. The N and C termini are marked N and C, respectively. The structure of the loop 108–123 connecting strands β2 and β3 could not be resolved. () Structure of UNC119 viewed after a 90° rotation around the vertical axis. * Figure 2: Interaction of UNC119 with Tα polypeptides. () Pulldown of rod and cone Tα with GST-UNC119 (representative Coomassie-stained gel of four independent experiments). Bound polypeptides from bovine retina lysates were analyzed by SDS-PAGE (lane 1, recombinant GST (G); lane 2, pulldown with GST as a control (GC); lane 3, GST-UNC119 pulldown (GU); lane 4, GST-UNC119 fusion protein). An asterisk identifies the proteins pulled down by GST-UNC119. () Identification of peptides by LC-MS/MS. The 40-kDa polypeptides pulled down with GST-UNC119 were identified by LC-MS/MS. Identified peptide sequences, shown in red, were matched with rod and cone Tα. () GST-UNC119 pulldown of Tα from wild-type (WT, lanes 1–4) and Gnat1−/− retinas (lanes 5–8). Lanes 1, 2, 5 and 6, input; lanes 3 and 7, control pulldowns with GST; lanes 4 and 8, pulldowns with GST-UNC119. Acylated Tα was pulled down (lane 4), but not farnesylated Tγ (lane 8). () GST-UNC119 pulldown of Tα and Tα(G2A) expressed in HEK cells (lanes 1–3, input; lanes 4�! ��6, pulldowns; lanes 1 and 4, HEK cells expressing bovine Tα; lanes 2 and 5, HEK cells expressing bovine Tα(G2A); lanes 3 and 6, mouse retina lysates). Blot was probed with antibody to Tα. Note that UNC119 does not interact with non-acylated Tα(G2A). () Specificity of retina lysate pulldowns (lanes 1–3, mouse retina lysates (input); lanes 4–6, retina lysate pulldowns; lane 4, GST control; lane 5, 10 μg GST-UNC119 was added; lane 6, 10 μg GST-UNC119 with 0.1% Triton X-100 and 0.1% NP-40 (DT) present in the binding buffer). Top, blot probed with antibody to Tα. Bottom, same blot probed with antibody to GCAP1. Note that myristoylated GCAP1 does not interact with GST-UNC119. * Figure 3: UNC119 is an acyl-binding protein. () GST-UNC119 pulldowns and inhibition by an acylated N-terminal Tα peptide (lane 1, retina lysate; lanes 2–4, glutathione bead pellets of retina lysates that were incubated with GST-UNC119, in the absence of peptide (lane 2), the presence of lauroyl-GAGASAEEKH (lane 3), and in the presence of non-acylated GAGASAEEKH peptide (lane 4); lanes 5–7, supernatants of 2–4). Note that lauroyl-GAGASAEEKH competed for binding (lane 3), but the non-acylated peptide did not (lane 4). () ITC. Human UNC119 was titrated with G protein α subunit N-terminal peptides. Red symbols, titration with N-terminal Tα peptide (lauroyl-GAGASAEEKH); black circles, titration with non-lauroylated GAGASAEEKH; green triangles, titration with myristoylated GAGASAEEKH; blue squares, titration with ODR-3 N-terminal peptide lauroyl-GSCQSNENSE. Lauroyl-GAGASAEEKH (red) and myristoyl-GAGASAEEKH (green) peptides were fit to a one-site model and bound with KD values of 0.54 ± 0.28 μM and 0.22 ± 0.14 μM! , respectively. Lauroyl–ODR-3 (blue) binding was more than one order of magnitude weaker (16.4 ± 3.0 μM). () Alignment of N-terminal peptides of mouse G protein α subunits, C. elegans G protein α subunits GPA-13 and ODR-3, and Ca2+-binding proteins GCAP1, GCAP2 and recoverin. Peptides lacking Gly at position 2 cannot be myristoylated, therefore interaction with UNC119 through an acyl chain does not extend to all subfamilies of Gα. * Figure 4: The lipid-binding pocket of UNC119. (,) Two orientations of UNC119 co-crystallized with the acylated Tα peptide in the UNC119 hydrophobic cavity. The lauroyl chain is shown in green and the ten amino acids of the peptide are modeled in dark gray. In , UNC119 is viewed after a 90° rotation around the vertical axis and the individual β strands are labeled β1–9 in yellow. () Stereoview of UNC119 residues and key water molecules interacting with the lauroyl-GAGASAEEKH ligand. The hydrogen-bonding network (black dashed lines) limits the depth to which the Tα peptide can penetrate UNC119. Hydrogen bonds were included if the average of the bond length for all six molecules in the asymmetric unit was 3.2 Å or less and satisfied appropriate hydrogen bonding stereochemistry. UNC119 residues are shown in yellow, the lauroyl chain is green and the attached residues are colored dark gray. Figures were created with PyMOL (http://www.pymol.org/). * Figure 5: UNC119 interacts with Tα-GTP and inhibits GTPase activity. () Extraction of Tα from membranes by UNC119. Live mice were exposed to 10,000 lx over 20 min, driving transducin to the inner segments. Retina lysates in 1× phosphate-buffered saline (PBS) were incubated with either GST, mUNC119, GST and GTP, or mUNC119 and GTP, respectively. The soluble proteins (S) were separated from membrane-bound proteins (P) by centrifugation. Tα was detected by western blot using antibody to Tα. Tα elutes only in the presence of UNC119 and GTP. () Pulldown assays with light-adapted and dark-adapted mouse retinas. PBS/GTP supernatants from retinas of a light-adapted mouse (2,000 lx) and hypotonic supernatants from retinas of a dark-adapted mouse were used for pulldown assays, respectively. The proteins pulled down by GST or GST-UNC119 (pellet) and unbound proteins (supernatant) were analyzed by western blot using antibody to Tα. GST-UNC119 binds Tα-GTP (left), but not Tα-GDP–Tβγ (right). () GTPase activity of purified Tαβγ in the presen! ce of ROS membranes. The activity of the reconstituted system (red line) corresponded to a rate of 1.5 mol GTP per min. Addition of bovine serum albumin (light blue) had little effect, whereas addition of UNC119 (green) reduced the activity nearly to baseline. Baseline activity was caused by a low amount of Tαβγ still attached to the membranes (see inset). Inset, SDS-PAGE of purified native transducin (only Tα and Tβ subunits are shown), recombinant human UNC119 and depleted ROS membranes containing rhodopsin and a trace of transducin (only Tα is visible, Tβ co-migrates with rhodopsin). * Figure 6: Slow return of transducin to the outer segment after intense light exposure. (,) Localization of Tα (green) in dark-adapted wild-type and Unc119−/− retina. Mice were dark-adapted for at least 12 h. Frozen sections were probed with antibody to Tα and FITC-linked secondary antibody. Note the presence of Tα in dark-adapted inner segments. (–) Mice were first exposed to intense light for 60 min, then dark-adapted for 3 h (,), 6 h (,) and 24 h (,). Frozen sections were probed with antibody to Tα and FITC-linked secondary antibody. Note that Tα slowly returned to the wild-type outer segment, but is blocked in part from returning to the Unc119−/− outer segment. IS, inner segment; ONL, outer nuclear layer; OS, outer segment. () Quantification of inner segment fluorescence at 0, 3, 6, and 24 h after start of dark-adaptation. Fluorescence signal was quantified using ImageJ software. Each bar included three independent measurements. Error bars denote means ± s.d. * Figure 7: Mislocalization of the G proteins ODR-3 and GPA-13 in a C. elegans unc-119(ed3) mutant. (–) Wild-type (,) and unc-119 mutant (,) C. elegans were stained with an antibody to ODR-3 (,) and GPA-13 (,). Mislocalization of ODR-3 and GPA-13 to the olfactory cell bodies and axons is evident in unc-119 mutants. (–) Cell-specific rescue of unc-119 in C. elegans restores GPA-13 and ODR-3 localization. The unc-119 gene fused with GFP was driven by the gpa-13 promoter in ADF, ASH and AWC in unc-119 mutants. The transgenic (,) and unc-119 mutant control (,) were labeled with GPA-13 (,) or ODR-3 antibodies (,). Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Protein Data Bank * 3GQQ * 3RBQ * 3GQQ * 3RBQ Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Houbin Zhang & * Ryan Constantine Affiliations * Department of Ophthalmology, University of Utah Health Science Center, Salt Lake City, Utah, USA. * Houbin Zhang, * Ryan Constantine, * Cecilia D Gerstner & * Wolfgang Baehr * Graduate Program in Neuroscience, University of Utah Health Science Center, Salt Lake City, Utah, USA. * Ryan Constantine * Department of Biological Sciences, Northeast Structural Genomics Consortium, Columbia University, New York, New York, USA. * Sergey Vorobiev, * Yang Chen, * Jayaraman Seetharaman & * Liang Tong * Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Northeast Structural Genomics Consortium, Rutgers University, Piscataway, New Jersey, USA. * Yuanpeng Janet Huang, * Rong Xiao & * Gaetano T Montelione * Department of Biology, University of Utah, Salt Lake City, Utah, USA. * M Wayne Davis, * Erik M Jorgensen & * Wolfgang Baehr * Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA. * George Inana * Department of Biochemistry, University of Utah School of Medicine, Salt Lake City, Utah, USA. * Frank G Whitby & * Christopher P Hill * Howard Hughes Medical Institute, University of Utah, Salt Lake City, Utah, USA. * Erik M Jorgensen * Department of Neurobiology and Anatomy, University of Utah Health Science Center, Salt Lake City, Utah, USA. * Wolfgang Baehr Contributions H.Z. generated pulldown/light-induced translocation results and is responsible for C. elegans immunostaining and imaging. R.C. generated ITC results. R.C., F.G.W. and C.P.H. generated human UNC119/acylated Tα-peptide co-crystals and determined the structure. S.V., Y.C., J.S., Y.J.H., R.X., G.T.M. and L.T. determined the unbound human UNC119 structure. R.C., C.D.G. and W.B. isolated ROS membranes, transducin and determined GTPase activity. M.W.D. and E.M.J. generated transgenic C. elegans mutants. G.I. generated the Unc119 knockout mouse. H.Z., R.C., C.P.H., L.T. and W.B. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Wolfgang Baehr Author Details * Houbin Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Ryan Constantine Search for this author in: * NPG journals * PubMed * Google Scholar * Sergey Vorobiev Search for this author in: * NPG journals * PubMed * Google Scholar * Yang Chen Search for this author in: * NPG journals * PubMed * Google Scholar * Jayaraman Seetharaman Search for this author in: * NPG journals * PubMed * Google Scholar * Yuanpeng Janet Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Rong Xiao Search for this author in: * NPG journals * PubMed * Google Scholar * Gaetano T Montelione Search for this author in: * NPG journals * PubMed * Google Scholar * Cecilia D Gerstner Search for this author in: * NPG journals * PubMed * Google Scholar * M Wayne Davis Search for this author in: * NPG journals * PubMed * Google Scholar * George Inana Search for this author in: * NPG journals * PubMed * Google Scholar * Frank G Whitby Search for this author in: * NPG journals * PubMed * Google Scholar * Erik M Jorgensen Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher P Hill Search for this author in: * NPG journals * PubMed * Google Scholar * Liang Tong Search for this author in: * NPG journals * PubMed * Google Scholar * Wolfgang Baehr Contact Wolfgang Baehr Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–5 and Supplementary Tables 1 and 2 Additional data - Synaptically driven state transitions in distal dendrites of striatal spiny neurons
- Nat Neurosci 14(7):881-888 (2011)
Nature Neuroscience | Article Synaptically driven state transitions in distal dendrites of striatal spiny neurons * Joshua L Plotkin1 * Michelle Day1 * D James Surmeier1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:881–888Year published:(2011)DOI:doi:10.1038/nn.2848Received10 January 2011Accepted06 April 2011Published online12 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Striatal spiny neurons (SPNs) associate a diverse array of cortically processed information to regulate action selection. But how this is done by SPNs is poorly understood. A key step in this process is the transition of SPNs from a hyperpolarized 'down state' to a sustained, depolarized 'up state'. These transitions are thought to reflect a sustained synaptic barrage, involving the coordination of hundreds of pyramidal neurons. Indeed, in mice, simulation of cortical input by glutamate uncaging on proximal dendritic spines produced potential changes in SPNs that tracked input time course. However, brief glutamate uncaging at spines on distal dendrites evoked somatic up states lasting hundreds of milliseconds. These regenerative events depended upon both NMDA receptors and voltage-dependent Ca2+ channels. Moreover, they were bidirectionally regulated by dopamine receptor signaling. This capacity not only changes our model of how up states are generated in SPNs, it also has f! undamental implications for the associative process underlying action selection. View full text Figures at a glance * Figure 1: State transitions can be triggered by local stimulation of distal dendrites. () Low (left) and high (right) magnification maximum-intensity projections of a dSPN filled with Alexa Fluor 568. Spines were stimulated with 1 ms uncaging pulses at 500 Hz and are numbered in the order in which they were stimulated. () Somatic voltage recording of the cell shown in in response to glutamate uncaging of a single spine (red) or all labeled spines in rapid succession (black). Black shaded region indicates timing of uncaging pulses. The timing of the single uncaging event is indicated by the red line below. An all-points histogram is shown in the inset. State membrane potentials are shown above as box plots (n = 56 cells). For all box plots, lines indicate median, boxes outline upper and lower quartiles and whiskers are the upper and lower 10% values. Shaded green boxes indicate state membrane potentials reported in a previous in vivo study5 and are shown for reference. () State transitions were evoked by uncaging at distal (black) dendritic spines; the same pro! tocol was then used to excite a proximal dendrite (red). () Somatic voltage traces that have been aligned to the last uncaging stimulus and normalized show that up states are readily induced by distal but not proximal activation. Box plots indicate the time between the end of uncaging and a 25% voltage fall (n = 8, *P = 0.002, Mann–Whitney rank sum test). () An example of a spine Ca2+ transient (top) and the corresponding somatic membrane potential (bottom) of an up state in a dSPN. The Ca2+ transient was measured using the low-affinity Ca2+ indicator Fluo 4FF as the change in Fluo 4FF fluorescence (ΔF) divided by the baseline Fluo 4FF fluorescence (Fo). () The experiment presented in repeated using the Ca2+ indicator Fluo 4FF (n = 9 cells, *P < 0.01, Mann–Whitney rank sum test). Up-state duration is plotted against the corresponding local distal dendritic Ca2+ transient (measured at the dendritic shaft around which spines were stimulated). * Figure 2: State transitions generated by proximal and distal dendritic glutamate uncaging are independent of the somatic EPSP size and internal solution used in the recording electrode. () Example of a state transition induced in a dSPN. Recording was made using a potassium gluconate–based internal electrode solution (115 mM potassium gluconate, 20 mM KCl, 10 mM sodium phosphocreatine, 10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer, 2 mM Mg-ATP and 0.3 mM Na-GTP)47 and Fluo 4FF. Laser power was tuned to evoke small (463 μV) somatic EPSPs from a 0.5-ms stimulation of a single distal dendritic spine (inset). The corresponding all-points histogram for this cell is shown to the right. () After a state transition is achieved, the up-state duration is not correlated with either the somatic EPSP induced by stimulation of a single distal dendritic spine or the recording internal solution used (n = 6 cells; internal solution, KMeSO4 or potassium gluconate with Fluo 4 or Fluo 4FF). () Although they require a higher stimulation density, up states induced using low-amplitude (463–774 μV; n = 6 cells) somatic EPSPs are similar to those ind! uced by supra-millivolt EPSPs (see Fig. 1). () In one case, a modest state transition was produced by the repetitive stimulation of a single distal spine (16 stimuli, 0.5 ms duration, 500 Hz). The average somatic EPSP evoked by a single stimulus and the corresponding all-points histogram are shown to the right. UC, uncaging. In box plots, lines indicate median, boxes outline upper and lower quartiles and whiskers are the upper and lower 10% values. * Figure 3: State transition generation is similar in dSPN and iSPNs. (,) State transitions were generated by glutamate uncaging at locations indicated by asterisks on distal dendrites of dSPNs (left) and iSPNs (right). High magnification maximum-intensity projection images showing the location of glutamate uncaging () and the resulting somatic voltage traces (). Insets show all-points histograms of membrane potentials for each trace. () Somatic membrane potentials of up and down states in dSPNs (n = 56 cells) and iSPNs (n = 18 cells). Data from Figure 1b are shown for comparison in shaded boxes in Figure 3c. (,) The length of spiny distal dendrite and the stimulation density (number of stimuli per 1 μm dendritic length) required to evoke state transitions are similar in dSPNs (n = 39) and iSPNs (n = 11). () No obvious differences were observed in the relationship between stimulus density and up-state duration in D1 (n = 39) or D2 (n = 11) SPNs. In box plots, lines indicate median, boxes outline upper and lower quartiles and whiskers are the ! upper and lower 10% values. * Figure 4: State transition generation requires NMDA receptors and voltage-gated Ca2+ channels. () State transitions were generated in dSPNs by glutamate uncaging on distal dendritic spines, and then the response to the same uncaging protocol was measured in the presence of the NMDA receptor antagonist AP5 (100 μM). Voltage traces are normalized to maximum amplitude and overlain. Traces are aligned to the end of the last uncaging pulse and normalized to the maximum amplitude of the state transition, as shown in Figure 1d. Box plots indicate the time between the end of uncaging stimulation and a 25% voltage fall. AP5 significantly reduced state transition duration, abolishing it in most cases (n = 5, P < 0.05, Mann–Whitney rank sum test). In all box plots, lines indicate median, boxes outline upper and lower quartiles and whiskers are the upper and lower 10% values. (–) State transitions were generated in dSPNs by glutamate uncaging on distal dendritic spines, and then the response to the same uncaging protocol was measured in the presence of NiCl2 (; 50 μM; n = 8! ), mibefradil (Mb, ; 20 μM; n = 8) or SNX-482 (SNX, ; 0.3 μM; n = 6). Box plots indicate the time between the end of uncaging stimulation and a 25% voltage fall. All three agents significantly reduced the somatic state transition duration (*P < 0.05, Mann–Whitney rank sum test). * Figure 5: NMDA acts at distal dendritic synapses to enable state transition generation. () Somatic voltage recordings of spontaneous depolarizations in a dSPN before and after application of 10 μM NMDA + 10 μM glycine + 10 μM gabazine (SR95531) ('NMDA cocktail', top) show an increase in excitatory inputs, which is prevented by 1 μM TTX (bottom). () Somatic voltage of a dSPN in response to suprathreshold current injection in the presence and absence of NMDA cocktail. No regenerative changes in somatic voltage were observed, implying both the location of up-state duration and the NMDA receptors involved in their maintenance are located distally. () State transitions were evoked by glutamate uncaging on distal dendritic spines of dSPNs, then again in the presence of 10 μM NMDA cocktail. Somatic up-state duration was significantly enhanced (n = 6, *P < 0.05, signed-rank test). The shaded region indicates the timing of glutamate uncaging. Traces are normalized to the maximum amplitude. Box plots indicate the time between the end of uncaging and a 25% voltage fa! ll; lines indicate median, boxes outline upper and lower quartiles and whiskers are the upper and lower 10% values. () The increase in up-state duration in dSPNs caused by NMDA cocktail is not occluded by 1 μM TTX, suggesting that the action of NMDA is directly on distal SPN dendrites and does not rely on action-potential-dependent glutamate release (n = 4). () A state transition was induced by stimulation of ten distal dendritic spines (twice each) in a dSPN. The up state could be maintained by supplemental stimulation of these same spines (once each) every 50 ms. Uncaging pulses are indicated by shaded regions. * Figure 6: Neuron modeling supports synergistic action of NMDA receptors and T- and R-type Ca2+ channels in up-state generation and maintenance. () NEURON model of an SPN containing 6 primary (1°) dendrites, 12 secondary (2°) dendrites and 24 tertiary (3°) dendrites. Four of the 3° dendrites are invested with spines and indicated by thick blue lines (s3°). The other twenty 3° dendrites do not contain localized spines (ns3°). All 3° dendrites have diameters tapering from 2.5–0.3 μm. AMPA and NMDA receptors are located in spines only; Cav3.2 channels are located in spines and tertiary dendrites, except where noted in ; R-type channels are located only in tertiary spines, except where noted in . () Simulations were run using the same stimulation protocol described experimentally, and the resulting membrane potential is shown for the first stimulated spine, the stimulated spiny 3° dendrite, the corresponding parent 2° dendrites of a stimulated and unstimulated 3° dendrite, and the soma. (–) Simulations were run in 'control' conditions and in the absence of functional NMDA receptors (NMDARs) (), R-type cha! nnels () or Cav3.2 T-type channels (). Voltage traces are of the first stimulated spine (top), the activated tertiary dendrite (middle) and the soma (bottom). () Simulations were run using the same protocol, gradually increasing the spine 'leak' conductance (Kleak) from 0 to 10−3 S cm−2. Increasing the leak conductance, effectively decreasing the input impedance, reduces the membrane potential amplitude and plateau duration in the stimulated spine (top), 3° dendrite (middle) and soma (bottom). Shaded regions indicate timing of stimulation. * Figure 7: Up-state duration is modulated by dopamine and adenosine receptor signaling. () Example of normalized traces of an up state induced by stimulation of distal dendritic spines in a dSPN before and after application of the D1 agonist 6-CPB (5 μM). Action potentials are truncated to better display state transitions. The shaded region indicates the timing of glutamate uncaging. The box plots to the right show the time between the end of uncaging stimulation and a 25% voltage fall. In all dSPNs examined, 6-CPB increased up-state duration (n = 8; *P = 0.008, signed-rank test). Before and after data points from the same cells are connected with lines. () Example of normalized traces of an up state induced by stimulation of distal dendritic spines in an iSPN before and after application of the D2 agonist quinpirole (Quin; 20 μM) and A2a receptor antagonist SCH58261 (200 nM). The shaded region indicates the timing of glutamate uncaging. The box plots (time from the end of uncaging stimulation to a 25% voltage fall) to the right show that quinpirole plus SCH5! 8261 reduces up-state duration in iSPNs (n = 6, *P < 0.05, signed-rank test). Before and after data points from the same cells are connected with lines. () Box plots showing the up-state duration as a percentage of control in D1 and D2 SPNs by 6-CPB (from ), CGS21680 (200 nM; n = 6, *P < 0.05, signed-rank test), SCH58261 (n = 5, signed rank test) and quinpirole + SCH58261 (from ). In box plots, lines indicate median, boxes outline upper and lower quartiles and whiskers are the upper and lower 10% values. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA. * Joshua L Plotkin, * Michelle Day & * D James Surmeier Contributions J.L.P. conducted experiments and data analysis; M.D. conducted the experiments in cortical pyramidal neurons and provided technical assistance with uncaging; D.J.S. supervised the project; and D.J.S. and J.L.P. designed the experiments and prepared the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * D James Surmeier Author Details * Joshua L Plotkin Search for this author in: * NPG journals * PubMed * Google Scholar * Michelle Day Search for this author in: * NPG journals * PubMed * Google Scholar * D James Surmeier Contact D James Surmeier Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–9 Additional data - Imaging analysis of clock neurons reveals light buffers the wake-promoting effect of dopamine
- Nat Neurosci 14(7):889-895 (2011)
Nature Neuroscience | Article Imaging analysis of clock neurons reveals light buffers the wake-promoting effect of dopamine * Yuhua Shang1, 2 * Paula Haynes2 * Nicolás Pírez2 * Kyle I Harrington3 * Fang Guo1, 2 * Jordan Pollack3 * Pengyu Hong3 * Leslie C Griffith2 * Michael Rosbash1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:889–895Year published:(2011)DOI:doi:10.1038/nn.2860Received09 March 2011Accepted17 May 2011Published online19 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg How animals maintain proper amounts of sleep yet remain flexible to changes in environmental conditions remains unknown. We found that environmental light suppressed the wake-promoting effects of dopamine in fly brains. The ten large lateral-ventral neurons (l-LNvs), a subset of clock neurons, are wake-promoting and respond to dopamine, octopamine and light. Behavioral and imaging analyses suggested that dopamine is a stronger arousal signal than octopamine. Notably, light exposure not only suppressed l-LNv responses, but also synchronized responses of neighboring l-LNvs. This regulation occurred by distinct mechanisms: light-mediated suppression of octopamine responses was regulated by the circadian clock, whereas light regulation of dopamine responses occurred by upregulation of inhibitory dopamine receptors. Plasticity therefore alters the relative importance of diverse cues on the basis of the environmental mix of stimuli. The regulatory mechanisms described here may con! tribute to the control of sleep stability while still allowing behavioral flexibility. View full text Figures at a glance * Figure 1: Light suppressed the wake-promoting effects of dopamine. (–) Induced firing of dopaminergic neurons markedly decreased sleep during the dark period in light-dark (LD) or constant darkness (DD) conditions followed by a sleep rebound the following day when firing was returned to normal levels. In constant darkness, sleep was even more severely suppressed, with both subjective daytime and nighttime sleep being almost entirely absent. TH-Gal4–driven expression of TrpA1 transiently increased the activity of dopaminergic neurons when the temperature was raised from 21 to 27 °C at the beginning of the night. The behavior was monitored for 3 d in either the light-dark condition or constant darkness at 27 °C before returning to 21 °C. For simplicity, only 1 d of data from each condition is shown. The data were collected from control UAS-TrpA1, control TH-Gal4 (green) and TH-Gal4; UAS-TrpA1 flies. Error bars represent s.d. * Figure 2: The l-LNvs form membrane contacts with dopaminergic and octopaminergic neurons. (,) The membrane-tethered GFP fragment construct CD4spGFP1-10 was expressed in most dopaminergic neurons with TH-Gal4, and CD4spGFP11 was expressed in l- and s-LNvs with Pdf-LexA. Green, GFP; red, PDF. The fine fibers in the ventral elongation in are likely to be the dendrites of the l-LNvs31. Reconstituted GFP signals were detected around the LNv cell bodies and dendritic area but not in the optical lobe around the axons of the l-LNvs (n = 6). The diagram indicates the orientation of the brain. D and M indicate the dorsal and medial sides of the brain, respectively. An image with higher magnification shows the reconstituted GFP signals around the LNv cell body and dendritic area (). Note that the PDF staining in the dendritic areas is very weak because the dendrites do not likely contain much of the PDF peptide, resulting in GFP that does not appear to colocalize well with PDF staining in the dendritic areas. (,) The membrane-tethered GFP fragment construct CD4spGFP1-10 was! expressed in most octopaminergic neurons with Tdc2-Gal4. Reconsitituted GFP signals were also detected around the LNv cell bodies and dendritic area (n = 10). Scale bars represent 10 μm. * Figure 3: The l-LNvs responded to dopamine or octopamine application by increasing cAMP. () Example of how FRET images were processed using an automated method as described in the Online Methods. Briefly, each video had two channels (YFP and CFP). The responses of a cell to a drug were computed as the mean of its CFP/YFP ratios, which were normalized by signals captured under the untreated condition. Cells without statistically significant response differences over time were merged as a group. In this example, the l-LNvs, but not the s-LNvs, increased cAMP in response to bath application of dopamine. () Dopamine (DA) application induced stronger responses in the l-LNvs than octopamine (OA). Left, imaging of flies reared in light-dark conditions (LD); right, flies reared in constant darkness day 1 (DD). () The responses could be induced by a dopamine agonist and were blocked by a dopamine antagonist. The average fluorescence change (area under the relative cAMP change curve) was determined by calculating an average CFP/YFP ratio increase from 100 to 445 s. Error ! bar represents s.e.m. Does this apply to error bars in b as well? Please state explicitly. A dopamine agonist, 100 μM pergolide mesylate, also induced an increase of cAMP in the l-LNvs with an effect only slightly less than dopamine alone. The l-LNv dopamine-induced cAMP response was almost completely blocked following a 15-min pre-incubation with a dopamine antagonist, 50 μM (+)-butaclamol hydrochloride. () Dopamine-induced responses were cell autonomous; the l-LNv responses to dopamine in both the presence and absence of TTX were indistinguishable. The l-LNvs increased cAMP level in response to bath application of dopamine in light-dark conditions. Responses of individual brain samples from different times of the day are shown. The relative cAMP changes are calculated as the normalized CFP/YFP ratio. Each curve represents the average cAMP response of all the visible l-LNvs in one hemisphere. The average cAMP responses from 13 brains are shown. Colored curves, with TTX a! dded to the acutely dissected brains before bath application o! f dopamine. Gray curves, responses recorded without TTX. * Figure 4: 12-h light exposure suppressed the responses of l-LNvs to dopamine or octopamine. (–) Light exposure suppressed the l-LNv responses to dopamine (DA). Flies were housed in light-dark conditions () or in constant darkness conditions () and the responses to dopamine during daytime or subjective day were compared with that during nighttime or subjective night. A summary of the relative changes of cAMP (,) is shown in . The l-LNv responses to dopamine during the day/subjective day versus the night/subjective night were not significantly different in either light-dark (LD) or constant darkness (DD) conditions. However, comparison between light-dark and constant darkness conditions showed that the responses of the l-LNvs to dopamine in constant darkness were much stronger during both the subjective day and subjective night than the responses at the same circadian times in light-dark conditions. (–) Daytime light exposure suppressed the nighttime l-LNv responses to octopamine (OA). Flies were housed in light-dark conditions () or constant darkness conditions ! () and the responses to octopamine during daytime or subjective day were compared with that during nighttime or subjective night. Note that the response amplitude of l-LNvs from subjective day in constant darkness was similar to that of daytime in light-dark conditions. A summary of the relative changes in cAMP (,) is shown in . The responses to octopamine during daytime, nighttime or subjective daytime were similar, whereas the l-LNvs from subjective night were more sensitive to octopamine. P values indicate significant difference from control groups (Student's t test). Error bars represent s.e.m. * Figure 5: The circadian clock (PER) specifically promotes octopamine-induced responses in l-LNvs at night. (–) The l-LNv responses to dopamine were not affected by PER. The daytime () and nighttime () responses are plotted separately. The dopamine-induced responses of the l-LNvs from control brains were compared with those from per01 mutant flies. A summary of the relative changes of cAMP (,) is shown in . The responses to dopamine were not affected by per01 mutation. (–) PER positively regulated octopamine-evoked responses by l-LNv at night. Flies were housed in light-dark conditions and the daytime () and nighttime () responses are plotted separately. The octopamine-induced responses of the l-LNvs from control brains were compared with those from per01 mutant flies. A summary of the relative changes of cAMP (,) is shown in . The responses to octopamine during daytime were not affected by per01 mutation (left), whereas the nighttime responses were markedly decreased in the per01 mutants (right). Error bars represent s.e.m. * Figure 6: Light suppresses dopamine responses by upregulating inhibitory dopamine receptors. (,) D2R negatively regulated dopamine-evoked responses in the l-LNvs. () The l-LNv response to dopamine in light-dark conditions was markedly increased by knocking down D2R expression in the l-LNvs. The dopamine-induced responses of the l-LNvs from control brains were compared with those from D2R-RNAi knockdown mutant flies. () The l-LNv response to dopamine in constant darkness conditions was not affected by knocking down D2R expression in the l-LNvs. The dopamine-induced responses of the l-LNvs from control brains were compared with those from D2R-RNAi knockdown mutant flies. () Summary of the relative changes of cAMP shown in and . The responses in constant darkness were comparable with those in D2R knockdown mutants in light-dark conditions. Error bars represent s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Howard Hughes Medical Institute, National Center for Behavioral Genomics, Brandeis University, Waltham, Massachusetts, USA. * Yuhua Shang, * Fang Guo & * Michael Rosbash * Department of Biology, Brandeis University, Waltham, Massachusetts, USA. * Yuhua Shang, * Paula Haynes, * Nicolás Pírez, * Fang Guo, * Leslie C Griffith & * Michael Rosbash * Department of Computer Science, Brandeis University, Waltham, Massachusetts, USA. * Kyle I Harrington, * Jordan Pollack & * Pengyu Hong Contributions Y.S. conceived the project. Y.S., P. Haynes, N.P. and F.G. performed the experiments. K.I.H., J.P. and P. Hong developed the algorithm for the automated imaging analysis. Y.S., L.C.G. and M.R. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Michael Rosbash Author Details * Yuhua Shang Search for this author in: * NPG journals * PubMed * Google Scholar * Paula Haynes Search for this author in: * NPG journals * PubMed * Google Scholar * Nicolás Pírez Search for this author in: * NPG journals * PubMed * Google Scholar * Kyle I Harrington Search for this author in: * NPG journals * PubMed * Google Scholar * Fang Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Jordan Pollack Search for this author in: * NPG journals * PubMed * Google Scholar * Pengyu Hong Search for this author in: * NPG journals * PubMed * Google Scholar * Leslie C Griffith Search for this author in: * NPG journals * PubMed * Google Scholar * Michael Rosbash Contact Michael Rosbash Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (467K) Supplementary Figures 1–7 Additional data - Social regulation of aggression by pheromonal activation of Or65a olfactory neurons in Drosophila
- Nat Neurosci 14(7):896-902 (2011)
Nature Neuroscience | Article Social regulation of aggression by pheromonal activation of Or65a olfactory neurons in Drosophila * Weiwei Liu2, 3, 7 * Xinhua Liang1, 3, 7 * Jianxian Gong4 * Zhen Yang4 * Yao-Hua Zhang5 * Jian-Xu Zhang5 * Yi Rao3, 6 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:896–902Year published:(2011)DOI:doi:10.1038/nn.2836Received03 February 2011Accepted14 April 2011Published online19 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg When two socially naive Drosophila males meet, they will fight. However, prior social grouping of males reduces their aggression. We found olfactory communication to be important for modulating Drosophila aggression. Although acute exposure to the male-specific pheromone 11-cis-vaccenyl acetate (cVA) elicited aggression through Or67d olfactory receptor neurons (ORNs), chronic cVA exposure reduced aggression through Or65a ORNs. Or65a ORNs were not acutely involved in aggression, but blockade of synaptic transmission of Or65a ORNs during social grouping or prior chronic cVA exposure eliminated social modulation of aggression. Artificial activation of Or65a ORNs by ectopic expression of the Drosophila gene TrpA1 was sufficient to reduce aggression. Social suppression of aggression requires subsets of local interneurons in the antennal lobe. Our results indicate that activation of Or65a ORNs is important for social modulation of male aggression, demonstrate that the acute and ch! ronic effects of a single pheromone are mediated by two distinct types of ORNs, reveal a behaviorally important role for interneurons and suggest a chemical method to reduce aggression in animals. View full text Figures at a glance * Figure 1: Social regulation of aggression mimicked by male odor or cVA. () Schematic illustration of social exposure. Group I males were isolated in nylon mesh tubes without exposure to other flies, group II males were isolated in nylon mesh tubes and exposed to mature virgin females, and group III males were isolated in nylon mesh tubes and exposed to other mature virgin males. () Schematic illustration of chronic cVA exposure. In the experimental group, a capillary containing 2–3 μl of cVA was inserted into each tube. In the control group, male flies were not exposed to cVA. (–) Chronic male odor or cVA exposure reduced aggression. Chronic male exposure for 1 d lengthened fighting latency () and reduced lunging frequency () (Kruskal-Wallis test, followed by Dunns post test, ***P < 0.001; NS, not significant, P > 0.05). cVA exposure for 6 h slightly reduced aggression, whereas exposure for 24 h or more significantly suppressed aggression (,) (Kruskal-Wallis test, followed by Dunns post test, *P < 0.05, **P < 0.01, ***P < 0.001). All values! are mean + s.e.m. Numbers below each bar represent the number of pairs of flies tested. * Figure 2: Electrophysiological responses to cVA in isolated, grouped and cVA-primed flies. () Illustration of the EAG recording site on the left antenna (anterior view). () Averaged response waveforms of all recordings (n = 18, 15 and 13 for isolated, grouped and cVA-primed flies, respectively) when different concentration of cVA was applied. Scale bar represents 0.5 mV and 1 s. (,) Modulus of response amplitude () and area of response waveforms () on cVA stimulation at different cVA concentrations. No significant differences were detected between isolated, grouped and cVA-primed flies at the concentration tested (Kruskal-Wallis test, P > 0.05). All values are mean + s.e.m. * Figure 3: Aggression-suppressing effect of chronic cVA exposure mediated by Or65a ORNs. (,) Schematic illustration of experimental design. (–) When Or67d-GAL4/+; UAS-Shits/+ and control flies were kept at 23 °C, chronic cVA exposure reduced their aggression. The aggression-suppressing effect of cVA could also be observed when the flies were shifted to 30 °C during the cVA exposure time window (Mann-Whitney test, *P < 0.05, **P < 0.01, ***P < 0.001). At 23 °C, cVA exposure could suppress aggression of Or65a-GAL4/+; UAS-Shits/+ and Or65a-GAL4/UAS-Shits flies (Mann-Whitney test). When Or65a-GAL4/+; UAS-Shits/+ and Or65a-GAL4/UAS-Shits flies were shifted to 30 °C, cVA exposure during this time window could no longer suppress aggression. (II) and (III) denote chromosomes of Or65a-GAL4 transgene insertions. All values are mean + s.e.m. Numbers below each bar represent the number of pairs tested. * Figure 4: Aggression-suppressing effect of chronic male exposure mediated by Or65a ORNs. (,) Schematic illustration of experimental design. (–) At 23 °C, 1 d of male exposure reduced aggression of flies of all genotypes. When the first four genotypes of flies were shifted to 30 °C during the male exposure time window, their aggression was also significantly reduced (Mann-Whitney test, *P < 0.05, **P < 0.01, ***P < 0.001). At 23 °C, 1 d of male exposure suppressed aggression of Or65a-GAL4/+; UAS-Shits/+ flies (Mann-Whitney test). When Or65a-GAL4/+; UAS-Shits/+ flies were shifted to 30 °C, male exposure during this time window no longer suppressed aggression. All values are mean + s.e.m. Numbers below each bar represent the number of pairs tested. * Figure 5: Or65a ORNs not required for aggression per se. () Schematic illustration of experimental design. Before eclosion, flies were reared at 18 °C. Newly eclosed male flies were isolated for 8 d. Half of the flies were kept at 18 °C, and the other half were heat-shocked (hs) at 29 °C. On day 9, aggression was assayed at 23 °C. () Schematic illustration of experimental design. All flies were reared and isolated at 23 °C for 8 d. On day 9, aggression of flies was assayed at either the permissive or restrictive temperature. (–) Killing or acute silencing Or65a ORNs did not affect aggression. When Or67d-GAL4/tub-GAL80ts; UAS-DTI/+ male flies were heat-shocked, their aggression was significantly reduced. Ablation Or65a ORNs did not affect aggression (,) (Mann-Whitney test, ***P < 0.001). When Or67d-GAL4/+;UAS-Shits/+ flies were assayed at 30 °C, their aggression was reduced (Mann-Whitney test). Acute disruption of synaptic transmission of Or65a ORNs did not affect aggression (,). All values are mean + s.e.m. Numbers below e! ach bar represent the number of pairs tested. * Figure 6: Aggression suppressed by chronic activation of Or65a ORNs. () Schematic illustration of experimental design. (,) Heat treatment for 2 d reduced the aggression of Or65a-GAL4/ UAS-TrpA1 flies (Mann-Whitney test, *P < 0.05, **P < 0.01). Aggression of other genotypes was not affected by the heat treatment. All values are mean + s.e.m. Numbers below each bar represent the number of pairs tested. * Figure 7: Local circuit plasticity in antennal lobe required for social modulation of aggression. When different combinations of GAL4 and UAS-Shits flies were kept at 23 °C, chronic cVA exposure significantly reduced aggression. Disruption synaptic transmission in NP3056-GAL4 neurons by shifting NP3056-GAL4/UAS-Shits flies to 30 °C during the cVA exposure time window totally eliminated the aggression-suppressing effect of chronic cVA exposure. A moderate attenuation of aggression-suppressing effect of chronic cVA exposure was detected in H24-GAL4, Kras-GAL4 and LCCH3-GAL4 lines, but not GH298-GAL4 and HB8-145–GAL4 lines. When synaptic transmissions in GH146-GAL4, NP225-GAL4 and MZ19-GAL4 neurons were blocked during cVA exposure time window, social modulation was not affected (Mann-Whitney test, *P < 0.05, **P < 0.01, ***P < 0.001). All values are mean + s.e.m. Numbers below each bar represent the number of pairs tested. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Weiwei Liu & * Xinhua Liang Affiliations * Institute of Neuroscience, Shanghai Institutes of Biological Sciences and Graduate School of the Chinese Academy of Sciences, Shanghai, China. * Xinhua Liang * Beijing Normal University College of Life Sciences, Beijing, China. * Weiwei Liu * National Institute of Biological Sciences, Beijing, China. * Weiwei Liu, * Xinhua Liang & * Yi Rao * Peking University School of Chemistry and Molecular Engineering, Beijing, China. * Jianxian Gong & * Zhen Yang * State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture, Institute of Zoology, Chinese Academy of Sciences, Beijing, China. * Yao-Hua Zhang & * Jian-Xu Zhang * Peking University School of Life Sciences State Key Laboratory of Membrane Biology, Beijing, China. * Yi Rao Contributions Y.R. conceived the project. Y.R., W.L. and X.L. designed the experiments. W.L. and X.L. performed the experiments. J.G. and Z.Y. synthesized cVA. Y.-H.Z. and J.-X.Z. performed the experiments shown in Supplementary Figure 1. W.L., X.L. and Y.R. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Yi Rao Author Details * Weiwei Liu Search for this author in: * NPG journals * PubMed * Google Scholar * Xinhua Liang Search for this author in: * NPG journals * PubMed * Google Scholar * Jianxian Gong Search for this author in: * NPG journals * PubMed * Google Scholar * Zhen Yang Search for this author in: * NPG journals * PubMed * Google Scholar * Yao-Hua Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Jian-Xu Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Yi Rao Contact Yi Rao Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–6 Additional data - Mushroom body efferent neurons responsible for aversive olfactory memory retrieval in Drosophila
- Nat Neurosci 14(7):903-910 (2011)
Nature Neuroscience | Article Mushroom body efferent neurons responsible for aversive olfactory memory retrieval in Drosophila * Julien Séjourné1 * Pierre-Yves Plaçais1 * Yoshinori Aso2, 3 * Igor Siwanowicz2 * Séverine Trannoy1 * Vladimiros Thoma2 * Stevanus R Tedjakumala2 * Gerald M Rubin3 * Paul Tchénio1, 4 * Kei Ito5 * Guillaume Isabel1 * Hiromu Tanimoto2, 6 * Thomas Preat1, 6 * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:903–910Year published:(2011)DOI:doi:10.1038/nn.2846Received21 December 2010Accepted11 April 2011Published online19 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Aversive olfactory memory is formed in the mushroom bodies in Drosophila melanogaster. Memory retrieval requires mushroom body output, but the manner in which a memory trace in the mushroom body drives conditioned avoidance of a learned odor remains unknown. To identify neurons that are involved in olfactory memory retrieval, we performed an anatomical and functional screen of defined sets of mushroom body output neurons. We found that MB-V2 neurons were essential for retrieval of both short- and long-lasting memory, but not for memory formation or memory consolidation. MB-V2 neurons are cholinergic efferent neurons that project from the mushroom body vertical lobes to the middle superiormedial protocerebrum and the lateral horn. Notably, the odor response of MB-V2 neurons was modified after conditioning. As the lateral horn has been implicated in innate responses to repellent odorants, we propose that MB-V2 neurons recruit the olfactory pathway involved in innate odor avoid! ance during memory retrieval. View full text Figures at a glance * Figure 1: MB-V2 neurons connect the mushroom body to the lateral horn and middle superior medial protocerebrum. (–) Distribution of mCD8::GFP (white, ) and Syt::GFP (white, ) in the MB-V2 neurons driven by NP2492. Neuropils were labeled with an antibody to synapsin (orange). Mushroom body (MB) vertical lobes are outlined (dashed lines in , , and ). () A membrane reporter mCD8::GFP was localized in the terminals of MB-V2 neurons (arrows) in the α- and α′-lobes of the mushroom body (), msmpr () and lateral horn (LH, ). () The presynaptic marker Syt::GFP was highly enriched in the terminals of MB-V2 neurons (arrows) in the msmpr () and in the lateral horn () but not in the mushroom body vertical lobes (). () Two subtypes of MB-V2 neurons, MB-V2α () and MB-V2α′ (), were identified by single-cell analyses. (,) Schematic diagrams show the two types of MB-V2 neurons (blue) relative to the mushroom body and lateral horn (light green). Dashed lines outline the brain surface. MB-V2α neurons formed arbors in the mushroom body α lobes () and projected to the msmpr and the dorsal part ! of the lateral horn (, arrow; n = 87). MB-V2α′ neurons formed arbors in the α′ lobes () and projected to the msmpr, the ventral part of the lateral horn (arrow) and to a region ventromedial to the lateral horn (; n = 44). (–) The polarity of the single MB-V2α neuron visualized by a flp-out clone driving mCD8::GFP and Syt::HA using NP2492. The terminals of a single MB-V2α neuron in the lateral horn (–) and the mushroom body and msmpr (–) are magnified. Scale bars represent 20 μm. * Figure 2: Output of the MB-V2 neurons is specifically required for the retrieval of short-lasting memory. Temperature shift protocols are shown above the graphs. () Blocking MB-V2 neuron output during retrieval impaired 2-h memory (top graph, NP2492, F2,39 = 4.937, P = 0.0123; bottom graph, MZ160, F2,36 = 3.935, P = 0.0185; ANOVA, n ≥ 12). NP2492/UAS-shits and MZ160/UAS-shits flies were significantly different from their respective genetic controls. () Blocking MB-V2 neuron output during training and consolidation did not affect 2-h memory (top graph, NP2492, F2,32 = 1.337, P = 0.28; bottom graph, MZ160, F2,33 = 1.796, P = 0.18; ANOVA, n ≥ 10). () Expression of Shits in MB-V2 neurons did not affect 2-h memory when flies were conditioned and tested at the permissive temperature (top, NP2492, F2,27 = 1.508, P = 0.24; bottom, MZ160, F2,27 = 1.101, P = 0.35; ANOVA, n ≥ 10). Data are mean performance indices ± s.e.m. NS, not significant (P ≥ 0.05); *P < 0.05. * Figure 3: Suppressing MB-V2 neurons in MZ160 rescues the memory defect. (–) Projection of the brain region including the mushroom body lobes (light green). In UAS-mCD8GFP;MZ160 flies (), terminals of MB-V2 in the α lobe were visualized (arrow), whereas the signal was strongly reduced in combination with Cha3.3kb-GAL80 () and NP2492-GAL80 (). Scale bars represent 20 μm. () Suppressing GAL4 expression in MB-V2 neurons with Cha3.3kb-GAL80 restored wild-type 2-h memory (F4,45 = 3.305, P = 0.0186). MZ160/UAS-shits flies differed significantly from UAS-shits, MZ160/+ and Cha3.3kb-GAL80/+;MZ160/UAS-shits flies. Cha3.3kb-GAL80/+;MZ160/UAS-shits flies did not differ significantly from UAS-shits, MZ160/+ and Cha3.3kb-GAL80/+;+/UAS-shits flies. n ≥ 10. () Suppressing GAL4 expression in MB-V2 neurons with NP2492-GAL80 restored wild-type 2-h memory (F2,36 = 5.774, P = 0.0067). MZ160/UAS-shits flies differed significantly from NP2492-GAL80/+;MZ160/UAS-shits flies. NP2492-GAL80/+;MZ160/UAS-shits flies did not differ significantly from NP2492-GAL80/+;+/UA! S-shits flies. Only females were used in this experiment. n ≥ 12. Data are mean performance indices ± s.e.m. ANOVA; *P < 0.05; **P < 0.01. * Figure 4: Output of the MB-V2 neurons is required for retrieval of consolidated long-lasting memories. Temperature shift protocols are shown above each graph. () Blocking MB-V2 neuron output during retrieval impaired 24-h memory after massed conditioning (top graph, NP2492, F2,39 = 4.822, P = 0.0134; bottom graph, MZ160, F2,27 = 12.95, P = 0.0001; ANOVA). NP2492/UAS-shits and MZ160/UAS-shits were significantly different from their respective genetic controls and did not differ significantly from 0 (one-sample t test, P = 0.44 and P = 0.72, respectively). n ≥ 10. () Blocking MB-V2 neuron output during retrieval impaired 24-h memory after spaced conditioning (top graph, NP2492, F2,30 = 5.469, P = 0.0094; bottom graph, MZ160, F2,38 = 6.247, P = 0.0037; ANOVA). NP2492/UAS-shits and MZ160/UAS-shits were significantly different from their respective genetic controls and NP2492/UAS-shits did not differ significantly from 0 (one-sample t test, P = 0.25). n ≥ 10. () Blocking MB-V2 neuron output during spaced training and 2 h after training did not affect 24-h memory. ANOVA reveale! d no significant difference among the groups with NP2492 (top, F2,27 = 0.1157, P = 0.89) and MZ160 (bottom, F2,27 = 0.1936, P = 0.83). n ≥ 10. Data show mean performance indices ± s.e.m. *P < 0.05; **P < 0.01; ***P < 0.005; NS, not significant. * Figure 5: MB-V2 neurons are cholinergic. (–) Cell bodies, including those of MB-V2 neurons in NP2492 flies, did not overlap with the markers for glutamate (DVGLUT, ), dopamine (tyrosine hydroxylase (TH), ) and GABA (GAD1, ). See Supplementary Figure 6 for consistent results with R71D08. (–) Terminals of the MB-V2 neurons in the lateral horn and msmpr colocalized with ChAT (arrows), whereas the signal was occasionally undetectable in some terminals (arrowheads). The processes of MB-V2 were visualized with mCD8::GFP driven by R71D08. Terminals of a population of MB-V2 neurons (–) or a single MB-V2 α neuron (–) were labeled with mCD8::GFP. See Supplementary Figure 5 for consistent results with NP2492. Scale bars represent 10 μm. * Figure 6: MB-V2 neurons in naive flies respond to olfactory stimuli. () Illustrative mean grayscale image of a time series acquisition showing a horizontal section of the right hemisphere of NP2492/+;UAS-GCaMP3/+ fly brain. MB-V2 neurons showed basal GCaMP3 fluorescence in their projections on the mushroom body vertical lobes (i), msmpr (ii), lateral horn (iii) and in cell bodies (iv). Dashed line encloses mushroom body and msmpr regions and was used to quantify odorant responses, here and in Figure 7. (–) Color-coded variations of fluorescence in the same fly, after 2-s exposure to benzaldehyde (), octanol (), methylcyclohexanol () and isoamylacetate (). MB-V2 neurons responded with calcium increase to all four odorants. Strong activation was observed in the mushroom body and msmpr area, as delimited in . The calcium influx was much lower when no odorant was diluted in the paraffin oil (). () Time course, averaged across all animals, of responses to octanol (red, n = 7), methylcyclohexanol (blue, n = 7) and oil (black, n = 5). The solid bl! ack bar indicates the delivery of the stimulus. () Peak responses for all odorants, calculated as the temporal mean over the time window shaded in gray (traces for benzaldehyde and isoamylacetate are not shown). There were no significant differences among the mean responses to isoamylacetate (I, n = 3), benzaldehyde (B, n = 3), octanol (O) and methylcyclohexanol (M), whereas the response to pure oil was significantly lower (one-way ANOVA, F4,220 = 26.56, P < 0.01). **P < 0.01; NS, not significant. * Figure 7: MB-V2 neurons show a reduced response to the trained odor after conditioning. () Overexpression of the GCaMP3 driven by NP2492-GAL4 did not significantly alter 3-h memory performance (n = 13, one-way ANOVA, F2,36 = 2, P = 0.15). () Sketch illustrating delivery of odors (black line) and electric shocks (red spikes) during the paired and unpaired training. (-) Two sets of reciprocal experiments, performed on flies trained with either 3-octanol (OCT) (,,,, ) or 4-methylcyclohexanol (MCH) (,,,,) as the CS+ odor ('CS+' in unpaired training). () Mean grayscale images (scale bars represent 15 μm) of MB-V2 projections to msmpr, and color-coded patterns of the responses obtained to both odors in representative paired (,) and unpaired (,) flies. Paired flies specifically show lower activation after CS+ than after CS− (arrowheads). Average time courses of odor responses are shown for paired (,) and unpaired (,) flies. Black bars indicate the delivery of the stimulus. In paired flies, during the time window shaded in gray, the time course of CS+ response was d! ecreased compared to the CS– response (, n = 7, P = 0.0035; , n = 6, P = 4.5 × 10−4), whereas 'CS+' and CS− responses remain undistinguishable in unpaired flies (, n = 7, P = 0.27; , n = 6, P = 0.13). The CS+ to CS− relative response, quantified as the natural logarithm of the CS+/CS− response, was lower in paired than in unpaired flies (, P = 0.0024; , P = 0.0043). **P < 0.01; NS, not significant. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Hiromu Tanimoto & * Thomas Preat Affiliations * Genes and Dynamics of Memory Systems, Neurobiology Unit, CNRS, Ecole Supérieure de Physique et de Chimie Industrielles, Paris, France. * Julien Séjourné, * Pierre-Yves Plaçais, * Séverine Trannoy, * Paul Tchénio, * Guillaume Isabel & * Thomas Preat * Max-Planck-Institut für Neurobiologie, Martinsried, Germany. * Yoshinori Aso, * Igor Siwanowicz, * Vladimiros Thoma, * Stevanus R Tedjakumala & * Hiromu Tanimoto * Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. * Yoshinori Aso & * Gerald M Rubin * Nanooptique et Physiologie Intégrée, Université Paris-Sud, CNRS UPR 3321, Orsay, France. * Paul Tchénio * Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo, Japan. * Kei Ito Contributions Y.A. and H.T. designed and I.S., S.R.T. and V.T. carried out anatomical experiments. I.S., Y.A. and H.T. analyzed the microscopic data and assembled figures. K.I. and G.M.R. provided Gal4 lines with their expression patterns. J.S., G.I. and T.P. designed and J.S., S.T. and P.-Y.P. carried out behavior experiments. J.S. generated the NP2492-Gal80 mutant. P.-Y.P., T.P. and P.T. designed the in vivo imaging experiments and P.-Y.P. carried them out. J.S., P.-Y.P., I.S., Y.A., H.T. and T.P. wrote the paper. T.P. and H.T. supervised the work. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Thomas Preat or * Hiromu Tanimoto Author Details * Julien Séjourné Search for this author in: * NPG journals * PubMed * Google Scholar * Pierre-Yves Plaçais Search for this author in: * NPG journals * PubMed * Google Scholar * Yoshinori Aso Search for this author in: * NPG journals * PubMed * Google Scholar * Igor Siwanowicz Search for this author in: * NPG journals * PubMed * Google Scholar * Séverine Trannoy Search for this author in: * NPG journals * PubMed * Google Scholar * Vladimiros Thoma Search for this author in: * NPG journals * PubMed * Google Scholar * Stevanus R Tedjakumala Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald M Rubin Search for this author in: * NPG journals * PubMed * Google Scholar * Paul Tchénio Search for this author in: * NPG journals * PubMed * Google Scholar * Kei Ito Search for this author in: * NPG journals * PubMed * Google Scholar * Guillaume Isabel Search for this author in: * NPG journals * PubMed * Google Scholar * Hiromu Tanimoto Contact Hiromu Tanimoto Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas Preat Contact Thomas Preat Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (3M) Expression pattern of NP2492 in the brain * Supplementary Video 2 (3M) Expression pattern of MZ160 in the brain * Supplementary Video 3 (2M) Expression pattern of MZ160, Cha3.3kb-GAL80 in the brain * Supplementary Video 4 (2M) Expression pattern of MZ160, NP2492-GAL80 in the brain * Supplementary Video 5 (3M) Expression pattern of R71D08 in the brain PDF files * Supplementary Text and Figures (17M) Supplementary Figures 1–9 Additional data - High-fat feeding promotes obesity via insulin receptor/PI3K-dependent inhibition of SF-1 VMH neurons
- Nat Neurosci 14(7):911-918 (2011)
Nature Neuroscience | Article High-fat feeding promotes obesity via insulin receptor/PI3K-dependent inhibition of SF-1 VMH neurons * Tim Klöckener1, 2, 3, 4, 5 * Simon Hess4, 6 * Bengt F Belgardt1, 2, 3, 4, 5 * Lars Paeger4, 6 * Linda A W Verhagen1, 2, 3, 4, 5 * Andreas Husch4, 6 * Jong-Woo Sohn7 * Brigitte Hampel1, 2, 3, 4, 5 * Harveen Dhillon9 * Jeffrey M Zigman7 * Bradford B Lowell9 * Kevin W Williams7 * Joel K Elmquist7, 8 * Tamas L Horvath10 * Peter Kloppenburg4, 6 * Jens C Brüning1, 2, 3, 4, 5 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:911–918Year published:(2011)DOI:doi:10.1038/nn.2847Received22 February 2011Accepted28 April 2011Published online05 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Steroidogenic factor 1 (SF-1)-expressing neurons of the ventromedial hypothalamus (VMH) control energy homeostasis, but the role of insulin action in these cells remains undefined. We show that insulin activates phosphatidylinositol-3-OH kinase (PI3K) signaling in SF-1 neurons and reduces firing frequency in these cells through activation of KATP channels. These effects were abrogated in mice with insulin receptor deficiency restricted to SF-1 neurons (SF-1ΔIR mice). Whereas body weight and glucose homeostasis remained the same in SF-1ΔIR mice as in controls under a normal chow diet, they were protected from diet-induced leptin resistance, weight gain, adiposity and impaired glucose tolerance. High-fat feeding activated PI3K signaling in SF-1 neurons of control mice, and this response was attenuated in the VMH of SF-1ΔIR mice. Mimicking diet-induced overactivation of PI3K signaling by disruption of the phosphatidylinositol-3,4,5-trisphosphate phosphatase PTEN led to incre! ased body weight and hyperphagia under a normal chow diet. Collectively, our experiments reveal that high-fat diet–induced, insulin-dependent PI3K activation in VMH neurons contributes to obesity development. View full text Figures at a glance * Figure 1: Insulin action in VMH neurons and generation of insulin receptorΔSF-1 mice. () SF-1 Cre-mediated recombination was visualized by immunohistochemistry for GFP in brains of SF-1GFP mice. A representative section is shown. () Double immunohistochemistry for β-galactosidase and PtdInsP3 on ventromedial hypothalamic neurons of SF-1lacZ and SF-1lacZ:ΔIR reporter mice in overnight-fasted mice that were intravenously injected with either saline or insulin and killed 10 or 20 min after stimulation. Representative section; scale bar, 10 μm. Blue (DAPI), DNA; red, β-gal (SF-1 neurons); green, PtdInsP3. (,) Quantification of PtdInsP3 immunoreactivity of SF-1 VMH neurons in SF-1lacZ () and SF-1lacZ:ΔIR () reporter mice after saline or insulin stimulation for either 10 or 20 min. Values are means ± s.e.m. of sections obtained from at least three mice per stimulation and genotype. We counted in total 4,400 neurons per genotype; quantification was performed as described in Online Methods. () In situ hybridization for Insr mRNA (red) expression in SF-1lacZ and! SF-1lacZ:ΔIR reporter mice. SF-1-positive cells were visualized by anti–β-galactosidase immunostaining (green), and nuclei were stained by DAPI (blue). Top, representative sections (scale bar, 10 μm); bottom, quantification of VMH insulin receptor mRNA expression in SF-1lacZ and SF-1lacZ:ΔIR mice normalized to that of SF-1lacZ mice. () Western blot analysis of insulin receptor-β subunit and α-tubulin (loading control) in hypothalamus (HT), rest of brain (RB), pituitary (Pit), liver, skeletal muscle (SM), pancreas (Panc), and white (WAT) and brown (BAT) adipose tissue in control and SF-1ΔIR mice (n = 4 in each group). * Figure 2: Effects of insulin on electrical activity of VMH neurons. () Representative recording of an identified insulin-responsive SF-1-positive neuron in a hypothalamic slice from an SF-1GFP mouse before and during addition of 200 nM insulin, followed by application of 200 μM tolbutamide (tolbut.). () Representative recording of an identified SF-1-positive neuron from a SF-1GFP:ΔIR mouse before and during addition of 200 nM insulin, followed by application of 200 μM tolbutamide. () Percentage of insulin-response SF-1 neurons from SF-1GFP mice and SF-1GFP:ΔIR mice. () Left, membrane potential of identified SF-1 neurons in hypothalamic slices from SF-1GFP mice before and during application of 200 nM insulin, followed by addition of 200 μM tolbutamide (n = 6 neurons per group). Right, firing frequency of identified SF-1 neurons in hypothalamic slices from SF-1GFP mice before and during application of 200 nM insulin, followed by addition of 200 μM tolbutamide (n = 5 neurons per group). () Left, membrane potential of identified SF-1 neuro! ns from SF-1GFP:ΔIR mice before and during application of 200 nM insulin, followed by addition of 200 μM tolbutamide (n = 12 neurons per group). Right, firing frequency of identified SF-1 neurons from SF-1GFP:ΔIR mice before and during application of 200 nM insulin, followed by addition of 200 μM tolbutamide (n = 9 neurons per group). Means ± s.e.m.; *P ≤ 0.05; **P ≤ 0.01. * Figure 3: Protection against diet-induced obesity in SF-1ΔIR mice. () Average body weight of male control and SF-1ΔIR mice on HFD and male control mice on normal chow diet (NCD) (n > 15 per genotype and diet). () Epigonadal fat pad of male control and SF-1ΔIR mice on NCD or HFD at the age of 20 weeks (n > 15 per genotype and diet). () Body fat content as measured by nuclear magnetic resonance spectroscopy of male control and SF-1ΔIR mice on NCD or HFD at the age of 20 weeks (n > 15 per genotype and diet). () Serum leptin levels of male control and SF-1ΔIR mice on NCD or HFD at the age of 8 and 20 weeks (n > 15 per genotype and diet). () Quantification of mean adipocyte surface in epigonadal adipose tissue of male control and SF-1ΔIR mice on NCD or HFD at the age of 20 weeks (n > 3 per group and diet). () Representative hematoxylin and eosin stain of epigonadal adipose tissue of male control and SF-1ΔIR mice on HFD at the age of 20 weeks. Scale bar, 100 μm. Means ± s.e.m.; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001; §P ≤ 0.01 for b! ody weight curve. * Figure 4: Increased leptin sensitivity in young SF-1ΔIR mice. () Daily food intake of male control and SF-1ΔIR mice on HFD at the age of 6 to 7 weeks (n > 16 per group). () Oxygen consumption of male control and SF-1ΔIR mice on HFD at the age of 6 to 7 weeks (n > 16 per group). () Daily food intake of male control and SF-1ΔIR mice on HFD at the age of 12 to 13 weeks (n > 14 per group). () Oxygen consumption of male control and SF-1ΔIR mice on HFD at the age of 13 to 14 weeks (n > 14 per group). () Leptin sensitivity of 7- to 8-week-old male control and SF-1ΔIR mice on HFD, 4 and 8 h after overnight fasting and intracerebroventricular injection of either artificial cerebrospinal fluid (ACSF) vehicle alone or 5 μg leptin in ACSF. *P ≤ 0.05 in an unpaired, one-tailed Student's t-test. Means ± s.e.m.; *P ≤ 0.05. * Figure 5: Protection against diet-induced insulin resistance in SF-1ΔIR mice. () Random fed blood glucose in male control and SF-1ΔIR mice on normal chow diet (NCD) and HFD at the age of 8 and 20 weeks (n > 14 per group). () Serum insulin in male control and SF-1ΔIR mice on NCD and HFD at the age of 8 and 20 weeks (n > 14 per group). () Intraperitoneal insulin tolerance test in male control and SF-1ΔIR mice on HFD at the age of 14 weeks (n > 18 per group). () Intraperitoneal glucose tolerance test in male control and SF-1ΔIR mice on HFD at the age of 15 weeks (n > 18 per group). Means ± s.e.m.; *P ≤ 0.05; **P ≤ 0.01. * Figure 6: Enhanced PI3K activation in the VMH promotes hyperphagia and weight gain. () Percentage of SF-1 VMH neurons with low, moderate and high PtdInsP3 immunoreactivity (as displayed in Fig. 1b; see Online Methods for quantification), determined by double immunohistochemistry for β-galactosidase and PtdInsP3 in ventromedial hypothalamic neurons of HFD-exposed SF-1lacZ and SF-1lacZ:ΔIR reporter mice after an overnight fast (n = 3 mice per group). () Percentage of neurons with low, moderate and high PtdInsP3, determined through double immunohistochemistry for β-galactosidase and PtdInsP3 in ventromedial hypothalamic neurons of normal chow diet–exposed SF-1lacZ (n = 3) and SF-1lacZ:ΔPTEN (n = 2) overnight-fasted reporter mice. () Average body weight of male control and SF-1ΔPTEN mice on normal chow diet (NCD) or HFD (n > 12 per genotype and diet), and SF-1ΔIR:ΔPTEN mice on HFD (SF-1ΔPTEN NCD versus control 1 NCD: *P < 0.05 for weeks 4–9, **P < 0.01 for weeks 10–20). () Daily food intake of male control and SF-1ΔPTEN mice on NCD at the age of ! 10 weeks (n = 8 per group). () Epigonadal fat pad mass of male control and SF-1ΔIR:ΔPTEN mice on HFD at the age of 20 weeks (n > 13 per genotype). () Quantification of mean adipocyte surface in epigonadal adipose tissue of male control and SF-1ΔIR:ΔPTEN mice on HFD at the age of 20 weeks (n > 3 per group). () Body fat content as measured by nuclear magnetic resonance spectroscopy of male control and SF-1ΔIR:ΔPTEN mice on HFD at the age of 20 weeks (n > 13 per genotype). Means ± s.e.m.; *P ≤ 0.05; **P ≤ 0.01. * Figure 7: Higher firing rate of POMC neurons of SF-1ΔIR mice upon high-fat feeding compared to controls. () Hypothalamic mRNA expression of Pomc, Agrp, Npy and Cartpt in control and SF-1ΔIR mice on HFD (n = 6 per genotype). () Hypothalamic mRNA expression of SF-1 (Nr5a1) and Bdnf in control and SF-1ΔIR mice on HFD (n = 6 per genotype). () Representative recording traces of POMC neurons in POMCGFP and POMCGFP;SF-1ΔIR mice on HFD. () Mean firing frequency of POMC neurons in POMCGFP and POMCGFP;SF-1ΔIR mice on HFD (n = 4 or 5 mice per genotype; n = 13 or 14 neurons per genotype). *P ≤ 0.05 in an unpaired, one-tailed Student's t-test. () Proportion of silent POMC neurons in POMCGFP and POMCGFP;SF-1ΔIR mice on HFD (n = 4 or 5 mice per genotype; n = 13 or 14 neurons per genotype). Means ± s.e.m.; *P ≤ 0.05. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Mouse Genetics and Metabolism, Institute for Genetics University of Cologne, Köln, Germany. * Tim Klöckener, * Bengt F Belgardt, * Linda A W Verhagen, * Brigitte Hampel & * Jens C Brüning * Center for Endocrinology, Diabetes and Preventive Medicine (CEDP), University Hospital Cologne, Köln, Germany. * Tim Klöckener, * Bengt F Belgardt, * Linda A W Verhagen, * Brigitte Hampel & * Jens C Brüning * Center for Molecular Medicine Cologne (CMMC), Köln, Germany. * Tim Klöckener, * Bengt F Belgardt, * Linda A W Verhagen, * Brigitte Hampel & * Jens C Brüning * Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (CECAD), Köln, Germany. * Tim Klöckener, * Simon Hess, * Bengt F Belgardt, * Lars Paeger, * Linda A W Verhagen, * Andreas Husch, * Brigitte Hampel, * Peter Kloppenburg & * Jens C Brüning * Max-Planck-Institute for Neurological Research, Köln, Germany. * Tim Klöckener, * Bengt F Belgardt, * Linda A W Verhagen, * Brigitte Hampel & * Jens C Brüning * Biocenter, Institute for Zoology and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Köln, Germany. * Simon Hess, * Lars Paeger, * Andreas Husch & * Peter Kloppenburg * Center for Hypothalamic Research, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA. * Jong-Woo Sohn, * Jeffrey M Zigman, * Kevin W Williams & * Joel K Elmquist * Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas, USA. * Joel K Elmquist * Center for Life Sciences, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA. * Harveen Dhillon & * Bradford B Lowell * Section of Comparative Medicine, Yale University School of Medicine, New Haven, Connecticut, USA. * Tamas L Horvath Contributions T.K., S.H., B.F.B., L.P., L.A.W.V., A.H., J.-W.S., B.H. and T.L.H. performed experiments, analyzed data and contributed to writing the paper. H.D., J.M.Z., B.B.L., K.W.W. and J.K.E. provided reagents and transgenic mice for this study. P.K. analyzed data and contributed to writing the paper. T.K. and J.C.B. conceived the study and wrote the manuscript. All authors read and agreed on the final version of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jens C Brüning Author Details * Tim Klöckener Search for this author in: * NPG journals * PubMed * Google Scholar * Simon Hess Search for this author in: * NPG journals * PubMed * Google Scholar * Bengt F Belgardt Search for this author in: * NPG journals * PubMed * Google Scholar * Lars Paeger Search for this author in: * NPG journals * PubMed * Google Scholar * Linda A W Verhagen Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Husch Search for this author in: * NPG journals * PubMed * Google Scholar * Jong-Woo Sohn Search for this author in: * NPG journals * PubMed * Google Scholar * Brigitte Hampel Search for this author in: * NPG journals * PubMed * Google Scholar * Harveen Dhillon Search for this author in: * NPG journals * PubMed * Google Scholar * Jeffrey M Zigman Search for this author in: * NPG journals * PubMed * Google Scholar * Bradford B Lowell Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin W Williams Search for this author in: * NPG journals * PubMed * Google Scholar * Joel K Elmquist Search for this author in: * NPG journals * PubMed * Google Scholar * Tamas L Horvath Search for this author in: * NPG journals * PubMed * Google Scholar * Peter Kloppenburg Search for this author in: * NPG journals * PubMed * Google Scholar * Jens C Brüning Contact Jens C Brüning Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (229K) Supplementary Figures 1–4 and Supplementary Tables 1–3 Additional data - Retinal origin of orientation maps in visual cortex
- Nat Neurosci 14(7):919-925 (2011)
Nature Neuroscience | Article Retinal origin of orientation maps in visual cortex * Se-Bum Paik1 * Dario L Ringach1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:919–925Year published:(2011)DOI:doi:10.1038/nn.2824Received28 January 2011Accepted23 March 2011Published online29 May 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The orientation map is a hallmark of primary visual cortex in higher mammals. It is not yet known how orientation maps develop, what function they have in visual processing and why some species lack them. Here we advance the notion that quasi-periodic orientation maps are established by moiré interference of regularly spaced ON- and OFF-center retinal ganglion cell mosaics. A key prediction of the theory is that the centers of iso-orientation domains must be arranged in a hexagonal lattice on the cortical surface. Here we show that such a pattern is observed in individuals of four different species: monkeys, cats, tree shrews and ferrets. The proposed mechanism explains how orientation maps can develop without requiring precise patterns of spontaneous activity or molecular guidance. Further, it offers a possible account for the emergence of orientation tuning in single neurons despite the absence of orderly orientation maps in rodents species. View full text Figures at a glance * Figure 1: Orientation maps as moiré interference patterns between retinal ganglion cell mosaics. () The superposition of two hexagonal lattices results in a periodic interference pattern. () Locally, patterns are organized into pairs of dipoles, in which cells of opposite center sign are nearest neighbors of each other. Cortical pooling of inputs from a dipole (relayed by the LGN) would result in simple-cell receptive fields with side-by-side ON and OFF subregions. () The period of the interference pattern is a function of the ratio between the lattice spacing in the two mosaics and their relative orientation. The operating points a, b and c lead to scaling factors of 21, 8.1 and 2.2, respectively, and these operating regimes are also used in the following figures. () Example of an orientation map generated from a moiré interference pattern. The left panel shows the moiré interference pattern between ON- and OFF-center receptive fields. Shaded areas on the moiré interference pattern show that dipoles with the same orientation arrange themselves as vertices of a hexag! onal lattice pattern (see also Fig. 2c). In the right panel, which represents the same area shown by the pattern on the left, the resulting cortical orientation tuning is shown in two ways. The left half of the map shows the preferred orientation of well tuned cortical cells (orientation selectivity index >0.25; see Online Methods) coded by their preferred orientation. The smooth map in the right half is obtained by Gaussian filtering of these strongly tuned orientation signals (see Online Methods for details). Dipole orientation (left panel) determines the preferred orientation of the best-tuned neurons in the cortex (right panel). Outlined white circles on the right panel correspond to the same iso-orientation domains depicted on the interference pattern on the left. * Figure 2: Moiré scaling factor and orientation map periodicity. Each column depicts examples of different scaling factors. The operating regimes illustrated are the ones shown by a, b and c in Figure 1c. () Examples of the resulting moiré interference patterns. () The preferred orientations of well tuned cells (left) and filtered orientation maps (right). Format as in Figure 1d. () Autocorrelations of orientation maps show hexagonal structure, indicating that iso-orientation domains lie on a hexagonal lattice (see also Fig. 1d). () Enlarged area from the maps in showing the predicted micro-architecture of orientation preference. Preferred orientation changes gradually in the left and middle panels. In the right panel, orientations are distributed as a salt-and-pepper-like pattern. () Histogram of the orientation differences between pairs of nearby cells (<100 μm) on the cortical surface. Similar orientations cluster in the left and middle panels. In the right panel, preferred orientations at nearby locations are uncorrelated. The unifo! rm distribution of angular differences in the right histogram is a signature of salt-and-pepper organization. Scale bar in , 1 mm on the retinal surface. Scale bars in –, 1 mm of cortical space. * Figure 3: Hexagonal structure of orientation maps. () The autocorrelation structure of orientation maps are shown for two different animals in four species. Secondary peaks (solid black circles) in the autocorrelation function form an approximate hexagonal structure in all cases. The magnitudes of all of these local maxima are statistically significant (bootstrap analysis, P < 0.002). The scale bar equals the orientation map period. () The average autocorrelation function across all animals shows local peaks (open white squares) that match closely the ones predicted by a perfect hexagonal lattice (open white circles). The solid black circles represent the locations of all the local maxima (shown in panel ) after the normalization step. White contour lines are plotted at a correlation coefficient of 0.33 to illustrate the separation of local peaks. The scale bar equals the orientation map period. () Angular location of local peaks in the autocorrelation function in panel relative to the reference peak. The distribution is bim! odal with modes near 60 and 120 degrees, as predicted by the model. Bimodality was established by a mixture of von Mises distributions using the Bayes information criterion to select the order of the model. The red solid line shows the probability distribution of the best fit. () The same analysis performed on control maps. Here the distribution of local peaks is much more isotropic. () One component (red line) is sufficient to account for the control data. In – local peaks were considered only if their distances to the origin were within ±33% of the map period. * Figure 4: Robustness of seeded map to positional noise. () The addition of independent Gaussian noise of an appropriate magnitude to the positions of vertices in the hexagonal lattice enables the model RGC mosaic to match the statistics of nearest-neighbor distance distributions observed experimentally, both within and across cell types. The experimental data show the distance between receptive field center locations. The standard deviation of the Gaussian noise required to match these distributions is σ = 0.12d. () The periodicity and strength of the seeded structure can be measured by the distance from the origin (black arrows) and magnitude of secondary peaks (white arrow) in the autocorrelation of the orientation map. As noise increases to realistic values, the secondary peak in autocorrelation remains strong and the map period is invariant, showing the robustness of the moiré interference pattern. The map period is plotted normalized to that attained in the absence of positional noise. () The percentage of ON–OFF dipoles! originally present in the noise-free interference pattern that are lost with increasing noise. The vertical line indicates the level required to match nearest-neighbor distributions, σ = 0.12d. For this value of noise, 27% of the dipoles are lost on average. () Dipoles that survive the perturbation of their location will have their original orientation perturbed. The histogram shows the distribution of changes in orientation in a dipole from its original orientation at experimental noise values, σ = 0.12d. * Figure 5: Robustness of receptive field shapes at different operating regimes of the model. Receptive field structure was evaluated by the distribution of (nx′,ny′) and of spatial phases of the simulated receptive fields. The distributions are similar for the different operating regimes, even though some do not support the existence of smooth orientation maps. Insets show the distributions of spatial phases in broadly tuned (bottom 50%) and sharply tuned (top 10%) simulated receptive fields. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, California, USA. * Se-Bum Paik & * Dario L Ringach * Department of Psychology, University of California, Los Angeles, California, USA. * Dario L Ringach Contributions Both S.-B.P. and D.L.R. were responsible for the theoretical concepts, computer simulations and writing. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Dario L Ringach Author Details * Se-Bum Paik Search for this author in: * NPG journals * PubMed * Google Scholar * Dario L Ringach Contact Dario L Ringach Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–4 and Supplementary Discussion Additional data - Cardinal rules: visual orientation perception reflects knowledge of environmental statistics
- Nat Neurosci 14(7):926-932 (2011)
Nature Neuroscience | Article Cardinal rules: visual orientation perception reflects knowledge of environmental statistics * Ahna R Girshick1, 2 * Michael S Landy1, 2 * Eero P Simoncelli1, 2, 3, 4 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:926–932Year published:(2011)DOI:doi:10.1038/nn.2831Received28 December 2010Accepted05 April 2011Published online05 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Humans are good at performing visual tasks, but experimental measurements have revealed substantial biases in the perception of basic visual attributes. An appealing hypothesis is that these biases arise through a process of statistical inference, in which information from noisy measurements is fused with a probabilistic model of the environment. However, such inference is optimal only if the observer's internal model matches the environment. We found this to be the case. We measured performance in an orientation-estimation task and found that orientation judgments were more accurate at cardinal (horizontal and vertical) orientations. Judgments made under conditions of uncertainty were strongly biased toward cardinal orientations. We estimated observers' internal models for orientation and found that they matched the local orientation distribution measured in photographs. In addition, we determined how a neural population could embed probabilistic information responsible for! such biases. View full text Figures at a glance * Figure 1: Observer model for local two-dimensional orientation estimation. Each local edge has a true image orientation, θ. In the encoding stage, the observer obtains a visual measurement, m(θ), corrupted by sensory noise. In the decoding stage, a function (the estimator, black curve) is applied to the measurement to produce the estimated orientation (m(θ)). Because of the sensory noise, the estimated orientation will exhibit variability across repeated presentation of the same stimulus, and may also exhibit a systematic bias relative to the true orientation. * Figure 2: Derivation of the estimator (m(θ)). In all three grayscale panels, the horizontal axis is stimulus orientation (θ), the vertical axis is the measured orientation (m(θ)) and the intensity corresponds to probability. Upper left, the mean observer's prior, raised to the power of 2.25 and re-normalized for visibility, is independent of the measurements (that is, all horizontal slices are identical). Upper right, the conditional distribution, p(m|θ). Vertical slices indicate measurement distributions, p(m|θ1) and p(m|θ2), for two particular stimuli θ1 and θ2. The widths of the measurement distributions are the average of those for the low- and high-noise conditions for the mean observer (multiplied by a factor of 10 for visibility). Horizontal slices, p(m1|θ) and p(m2|θ), describe the likelihood of the stimulus orientation, θ, for the particular measurements, m1 and m2. Note that the likelihoods are not symmetric, as the measurement distribution width depends on the stimulus orientation. Bottom, the poste! rior distribution is computed using Bayes' rule, as the normalized product of the prior and likelihood (top two panels). Horizontal slices correspond to posterior distributions p(θ|m1) and p(θ|m2), which describe the probability of a stimulus orientation given two particular measurements. Red dots indicate maximum a posteriori estimates (the modes of the posterior) for these two likelihoods, (m1) and (m2). Circular mean estimates yield similar results (see Supplementary Fig. 2). The red curve shows the estimator (m) computed for all measurements. An unbiased estimator would correspond to a straight line along the diagonal. * Figure 3: Stimuli and experimental results. () Stimuli are arrays of oriented Gabor functions (contrast increased for illustrative purposes). Left, a low-noise stimulus (L). Right, a high-noise stimulus (H) with mean orientation slightly more clockwise. Observers indicated whether the right stimulus was oriented counter-clockwise or clockwise relative to the left stimulus. () Variability for the same-noise conditions for representative subject S1 (left) and the mean subject (right), expressed as the orientation discrimination threshold (that is, JND). Mean subject values are computed by pooling raw choice data from all five subjects. Error bars indicate 95% confidence intervals. Dark gray and light gray curves are fitted rectified sinusoids, used to estimate the widths of the underlying measurement distributions. Pale gray regions indicate ± 1 s.d. of 1,000 bootstrapped fits. () Cross-noise (high noise versus low noise) variability data (circles). The horizontal axis is the orientation of the high-noise stimulus. () ! Relative bias, expressed as the angle by which the high-noise stimulus must be rotated counter-clockwise so as to be perceived as having the same mean orientation as the low-noise stimulus. * Figure 4: Example cross-noise comparison. The vertical axis is the measured orientation, m(θ), and the horizontal axis is estimated stimulus orientation, (m(θ)). Measurements corresponding to low-noise stimuli, mL(θL) (dark gray), or high-noise stimuli, mH(θH) (light gray), enter on the left. Each measurement is transformed by the appropriate nonlinear estimator (solid curves) into an estimate (bottom). The estimators correspond to those of the mean observer exaggerated for illustration as in Figure 2. The high-noise estimator exhibits larger biases than the low-noise estimator. The sensory noise of the measurements propagates through the estimator, resulting in estimator distributions (note these should not be confused with the posteriors). Comparison of these distributions produces a single point on the psychometric function. * Figure 5: Recovered priors for subject S1 and mean subject. The control points of the piecewise cubic spline (see Online Methods) are indicated by black dots. The gray error region shows ± 1 s.d. of 1,000 bootstrapped estimated priors. * Figure 6: Natural image statistics. () Example natural scene from Figure 1, with strongly oriented locations marked in red. () Orientation distribution for natural images (gray curve). * Figure 7: Comparison of human observers' priors and environmental distribution for subject S1 (left) and the mean subject (right). () Human observers' priors (black curves, from Fig. 5) and environmental distribution from natural images (medium gray curve, from Fig. 6b). () Cross-noise variability data (circles, from Fig. 3c) with predictions of the two Bayesian-observer models using each of the three priors shown in and the uniform prior. The uniform prior predicts little or no effect of stimulus orientation on discrimination (light gray curves). In contrast, both the environmental prior (medium gray curves) and the recovered human observers' priors (black curves) predict better discrimination at the cardinals, as seen in the human observers. () Relative bias data (circles, from Fig. 3d) with the predictions of Bayesian-observer models using three priors shown in . The uniform prior predicts no bias or a small bias in the opposite direction (for example, Subject S2 in Supplementary Fig. 1c). In contrast, both non-uniform priors predict the bimodal bias exhibited by human observers. () Normalized log li! kelihood of the data for Bayesian-observer models using two different priors: environmental distribution and the recovered observer's prior. Error bars denote the 5th and 95th percentiles from 1,000 bootstrap estimates. Values greater than 1 indicate performance better than that of the raw psychometric fits, whereas values less than 0 indicate performance worse than that obtained with a uniform prior. * Figure 8: Simulations of neural models with non-uniform encoder and population vector decoder. () Tuning curves of an encoder population with non-uniform orientation preferences and non-uniform tuning widths based on neurophysiology (only a subset of neurons shown). Neurons preferring 45 deg and 90 deg stimuli are highlighted in black. () Tuning curves of a population with non-uniform preferences and uniform widths. () Tuning curves of a population with uniform preferences and non-uniform widths. (–) Variability for the same-noise conditions for the populations in –: low noise versus low noise (dark gray) and high noise versus high noise (light gray). (–) Relative bias for the cross-noise condition (high noise versus low noise) for the populations in –. The fully non-uniform population () produces variability and bias curves similar to those exhibited by humans (Fig. 3b,d and Supplementary Fig. 1a,c). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Psychology, New York University, New York, New York, USA. * Ahna R Girshick, * Michael S Landy & * Eero P Simoncelli * Center for Neural Science, New York University, New York, New York, USA. * Ahna R Girshick, * Michael S Landy & * Eero P Simoncelli * Howard Hughes Medical Institute, New York University, New York, New York, USA. * Eero P Simoncelli * Courant Institute for Mathematical Sciences, New York University, New York, New York, USA. * Eero P Simoncelli Contributions A.R.G., M.S.L. and E.P.S. contributed to the design of the experiments, design of the analyses and the writing of the manuscript. A.R.G. conducted the experiments and performed the analyses. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ahna R Girshick Author Details * Ahna R Girshick Contact Ahna R Girshick Search for this author in: * NPG journals * PubMed * Google Scholar * Michael S Landy Search for this author in: * NPG journals * PubMed * Google Scholar * Eero P Simoncelli Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (520K) Supplementary Figures 1–5 Additional data - Neuronal basis of sequential foraging decisions in a patchy environment
- Nat Neurosci 14(7):933-939 (2011)
Nature Neuroscience | Article Neuronal basis of sequential foraging decisions in a patchy environment * Benjamin Y Hayden1, 2 * John M Pearson1, 2 * Michael L Platt1, 2, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:933–939Year published:(2011)DOI:doi:10.1038/nn.2856Received09 November 2010Accepted28 March 2011Published online05 June 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Deciding when to leave a depleting resource to exploit another is a fundamental problem for all decision makers. The neuronal mechanisms mediating patch-leaving decisions remain unknown. We found that neurons in primate (Macaca mulatta) dorsal anterior cingulate cortex, an area that is linked to reward monitoring and executive control, encode a decision variable signaling the relative value of leaving a depleting resource for a new one. Neurons fired during each sequential decision to stay in a patch and, for each travel time, these responses reached a fixed threshold for patch-leaving. Longer travel times reduced the gain of neural responses for choosing to stay in a patch and increased the firing rate threshold mandating patch-leaving. These modulations more closely matched behavioral decisions than any single task variable. These findings portend an understanding of the neural basis of foraging decisions and endorse the unification of theoretical and experimental work in ! ecology and neuroscience. View full text Figures at a glance * Figure 1: Patch-leaving task. () Task design. After fixation, two eccentric targets, a large gray and a small blue rectangle, appear. Monkey chooses one of two targets by shifting gaze to it. Choice of blue rectangle (stay in patch) yields a short delay (0.4 s, handling time) and reward whose value diminishes by 19 μl per trial. Choice of gray rectangle (leave patch) yields no reward and a long delay (travel time) whose duration is indicated by the height of the bar, and resets the value of the blue rectangle at 306 μl. Travel time varies randomly from patch to patch and ranges from 0.5 to 10.5 s. () Plot of the cumulative reward available in this task as a function of time in patch, given the search times associated with animals' performance in the task (black line). Data are generated on the basis of average times associated with performance. () Plot of reward intake rate derived from a range of patch residence times (x axis: range of residence times). Data are shown for each of ten travel times (1-s! intervals from 0.5 to 10.5 s). Rate-maximizing time in patch (the curves' maxima, shown by the black line) increases with increasing travel time. Data are generated based on average times associated with actual animal performance. * Figure 2: Monkeys obey the marginal value theorem in a virtual patchy foraging task. () Monkeys remain in the patch longer as travel time rises, as predicted by the marginal value theorem (MVT). Each dot indicates a single patch-leaving decision (n = 2,834 patch-leaving events). The time at which the monkey chose to leave the patch (y axis) was defined relative to the beginning of foraging in that patch. Travel time was kept constant in a patch (x axis). Data from both monkeys is shown. Behavior (average is traced by the blue line) closely followed the rate-maximizing leaving time (red line), albeit delayed by 0–2 s. () Performance of two monkeys on handling time variant of patch-leaving task. In this control experiment, travel time was held constant (5 s) and handling time was randomly reset between each patch to have one of ten values. Patch residence time fell as handling time rose, consistent with the MVT. Observed times are shown with black dots; averages are shown with solid blue line. Best-fit line (dashed blue line) is nearly identical to rate-maxi! mizing (red line). Average patch residence time on the interleaved standard travel time version of the task was consistent with this curve as well (red dots). * Figure 3: Firing rates of dACC neurons integrate patch residence time and travel time in computations occurring over multiple actions. () Average reward-aligned peri-stimulus time histograms (PSTHs) for example cell. Neuronal responses were briefly enhanced around the time of saccades and then fell to a baseline level between trials. Time zero indicates end of saccade, indicating choice. Dark gray box, pre-saccadic epoch. Light gray box, post-saccadic epoch. Black rectangle indicates the average duration of the trial. () The firing rate during the peri-saccadic period rose with time in patch. Each panel indicates responses selected from one range of patch residence times. (,) Average responses of example neuron () and population of neurons () occurring in a series of 5-s analysis epochs (gray box in ). Firing rates increased as time in patch increased. Error bars represent s.e.m. () Histogram of regression coefficients relating firing rate in pre-saccadic epoch to time in patch for each neuron in the population (n = 102). Significant effects are indicated with gray boxes (P < 0.05). * Figure 4: Firing rates of dACC neurons rise to a threshold associated with patch abandonment. () Plot of patch-leaving times, separated by whether they were earlier or later than the average leaving time. We divided patch-leaving decisions into four categories: earliest (black), early (red), late (cyan) and latest (magenta). These variables are independent of travel time and time in patch, meaning that, for example, earliest trials are equally likely to occur at any travel time (x axis) and any time in patch (y axis). () PSTH for an example neuron separated by earliness level. dACC neurons responded sooner and more strongly on earlier trials than on later trials. Black rectangle indicates the average duration of the trial. (,) Average firing rates of example neuron () and population () separated by earliness level. Firing rates rose faster for earlier patches but asymptoted at the same level. Error bars represent s.e.m. () Plot of average firing rate of population of neurons, aligned to final trial in patch (x = 0 on graph) and showing the final three trials before s! witch (x = 1, 2 and 3). Firing rates rose to the same level on final trial, as well as preceding trials. Error bars represent s.e.m. * Figure 5: Travel time governs both neuronal response gain and threshold. () Schematic of three possible mechanisms by which exogenous factors may govern a rise-to-threshold process. Shorter travel times can hasten patch-leaving (leftward movement on x axis) by increasing the rate of rise, reducing the threshold or elevating the baseline. (,) Evidence that travel times change rate of rise. Example neuron () and population () average regression slopes (beta weights) for firing rate as a function of time in patch. Beta weights fell as travel times rose, indicating that shorter travel time increases neuronal response gain. Error bars represent s.e.m. (,) Evidence that travel time influences firing threshold for patch abandonment. Firing rate on patch-leaving trial was taken as a proxy for threshold level. Example neuron () and population () show increasing firing rates on patch-leaving trial as travel time increases (black dots). Firing rate on penultimate trial also rose with travel time, consistent with a multi-trial integration process. Error bars! represent s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Neurobiology, Duke University School of Medicine, Durham, North Carolina, USA. * Benjamin Y Hayden, * John M Pearson & * Michael L Platt * Center for Cognitive Neuroscience, Duke University, Durham, North Carolina, USA. * Benjamin Y Hayden, * John M Pearson & * Michael L Platt * Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, USA. * Michael L Platt Contributions B.Y.H. designed the experiment and collected the data. B.Y.H. and J.M.P. contributed to data analysis. B.Y.H., J.M.P. and M.L.P. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Benjamin Y Hayden Author Details * Benjamin Y Hayden Contact Benjamin Y Hayden Search for this author in: * NPG journals * PubMed * Google Scholar * John M Pearson Search for this author in: * NPG journals * PubMed * Google Scholar * Michael L Platt Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (800K) Supplementary Figures 1–8 and Supplementary Refereneces Additional data
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