Latest Articles Include:
- Surprise! A unifying model of dorsal anterior cingulate function?
- Nat Neurosci 14(10):1219-1220 (2011)
Article preview View full access options Nature Neuroscience | News and Views Surprise! A unifying model of dorsal anterior cingulate function? * Tobias Egner1Journal name:Nature NeuroscienceVolume: 14,Pages:1219–1220Year published:(2011)DOI:doi:10.1038/nn.2932Published online27 September 2011 Few brain regions' functions have been debated as intensely as those of the dorsal anterior cingulate cortex. A computational model now suggests that seemingly diverse cingulate responses may be explained by a single construct, 'negative surprise', which occurs when actions do not produce the expected outcome. 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 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Tobias Egner is in the Department of Psychology and Neuroscience and the Center for Cognitive Neuroscience, Duke University, Durham, North Carolina, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Tobias Egner Author Details * Tobias Egner Contact Tobias Egner Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Regulation of complex I by Engrailed is complex too
- Nat Neurosci 14(10):1221-1222 (2011)
Article preview View full access options Nature Neuroscience | News and Views Regulation of complex I by Engrailed is complex too * Laurie H Sanders1 * J Timothy Greenamyre1 * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1221–1222Year published:(2011)DOI:doi:10.1038/nn.2939Published online27 September 2011 Engrailed, a homeobox transcription factor that is crucial for neuronal development, is now shown to regulate mitochondrial complex I and to be critical for the survival, protection and physiology of adult dopamine 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 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Laurie H. Sanders and J. Timothy Greenamyre are at the Pittsburgh Institute for Neurodegenerative Diseases and Department of Neurology, University of Pittsburgh School of Medicine and Pittsburgh Veterans Administration Healthcare System, Pittsburgh, Pennsylvania, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * J Timothy Greenamyre Author Details * Laurie H Sanders Search for this author in: * NPG journals * PubMed * Google Scholar * J Timothy Greenamyre Contact J Timothy Greenamyre Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Receptors in (e)motion
- Nat Neurosci 14(10):1222-1224 (2011)
Article preview View full access options Nature Neuroscience | News and Views Receptors in (e)motion * Jelena Radulovic1 * Natalie C Tronson1 * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1222–1224Year published:(2011)DOI:doi:10.1038/nn.2938Published online27 September 2011 A study finds that recall of fear-provoking memory changes the surface levels of AMPA receptors in the dorsal hippocampus. Inhibition of AMPA receptor trafficking strengthens the memory and results in excessive fear. 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 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Jelena Radulovic and Natalie C. Tronson are in the Department of Psychiatry and Behavioral Sciences, The Asher Center for Study and Treatment of Depressive Disorders, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jelena Radulovic Author Details * Jelena Radulovic Contact Jelena Radulovic Search for this author in: * NPG journals * PubMed * Google Scholar * Natalie C Tronson Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Is that a bathtub in your kitchen?
- Nat Neurosci 14(10):1224-1226 (2011)
Article preview View full access options Nature Neuroscience | News and Views Is that a bathtub in your kitchen? * Marius V Peelen1 * Sabine Kastner2 * Affiliations * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:1224–1226Year published:(2011)DOI:doi:10.1038/nn.2936Published online27 September 2011 We can efficiently and rapidly recognize daily-life visual settings. A study finds that scene recognition involves the posterior object-selective visual cortex, where multiple within-scene objects are represented in parallel. 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 * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Marius V. Peelen is at the Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy * Sabine Kastner is in the Department of Psychology and at the Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Marius V Peelen or * Sabine Kastner Author Details * Marius V Peelen Contact Marius V Peelen Search for this author in: * NPG journals * PubMed * Google Scholar * Sabine Kastner Contact Sabine Kastner Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Maintaining a Highwire act
- Nat Neurosci 14(10):1226 (2011)
Article preview View full access options Nature Neuroscience | News and Views Maintaining a Highwire act * Timothy SpencerJournal name:Nature NeuroscienceVolume: 14,Page:1226Year published:(2011)DOI:doi:10.1038/nn1011-1226Published online27 September 2011Corrected online27 September 2011 Haibei Zhang In all organisms with a nervous system, the construction of functional neural circuits requires a precise choreography of developmental events that includes the growth and guidance of axons to their proper targets and the formation of mature synapses. Studies in species ranging from worms to mice have revealed that proteins from the PHR (Pam/Highwire/RPM-1) family of E3 ubiquitin ligases modulate neural development via the formation of an F-box protein–containing complex and activation of the MAP kinase signaling cascade. Despite these advances in our understanding of the components and activities of these integral protein complexes, it is still unclear as to how the essential PHR proteins themselves are regulated during development. On page 1267, Tian and colleagues identify Rae1 as a binding partner of the Drosophila PHR protein Highwire (Hiw) that acts to prevent its degradation and promote refinement of the presynaptic terminal. 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. Change history Article tools * Print * Email * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Corrected online 27 September 2011In the version of this article initially published, the artist credit was omitted. The artist's name is Haibei Zhang. The error has been corrected in the HTML and PDF versions of the article. Additional data - Heterogeneity of CNS myeloid cells and their roles in neurodegeneration
- Nat Neurosci 14(10):1227-1235 (2011)
Nature Neuroscience | Review Heterogeneity of CNS myeloid cells and their roles in neurodegeneration * Marco Prinz1 * Josef Priller2 * Sangram S Sisodia3 * Richard M Ransohoff4 * Affiliations * Corresponding authorsJournal name:Nature NeuroscienceVolume: 13,Pages:1227–1235Year published:(2011)DOI:doi:10.1038/nn.2923Published online27 September 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 diseased brain hosts a heterogeneous population of myeloid cells, including parenchymal microglia, perivascular cells, meningeal macrophages and blood-borne monocytes. To date, the different types of brain myeloid cells have been discriminated solely on the basis of their localization, morphology and surface epitope expression. However, recent data suggest that resident microglia may be functionally distinct from bone marrow– or blood-derived phagocytes, which invade the CNS under pathological conditions. During the last few years, research on brain myeloid cells has been markedly changed by the advent of new tools in imaging, genetics and immunology. These methodologies have yielded unexpected results, which challenge the traditional view of brain macrophages. On the basis of these new studies, we differentiate brain myeloid subtypes with regard to their origin, function and fate in the brain and illustrate the divergent features of these cells during neurodegeneratio! n. View full text Figures at a glance * Figure 1: Myeloid cells in the CNS, their origin and their involvement in neurodegeneration. Microglia in the CNS (orange ramified cells) are predominantly yolk sac–derived from early embryonic days. In neurodegenerative diseases, bone marrow–derived phagocytes can engraft in the brain (green ramified cells). These myeloid cells originate from circulating Ly-6Chi monocytes or from bone marrow–derived progenitors, for example, granulocyte-macrophage progenitors (GMPs) or other progeny of hematopoietic stem cells (HSCs). Peripheral blood monocytes arise from macrophage/dendritic cell progenitors (MDPs) in the marrow, which also generate common dendritic cell progenitors (CDPs). Bone marrow–derived phagocytes, which are recruited into the brain as a result of neurodegenerative conditions, may be functionally distinct from microglia. In models of Alzheimer's disease and Huntington's disease, endogenous microglia degenerate, whereas bone marrow–derived phagocytes, including perivascular macrophages, decrease amyloid burden. In genetic models of ALS, motor neuro! n death is propagated by endogenous microglia expressing the mutant SOD1 protein and ROS, whereas wild-type (WT) bone marrow–derived phagocytes can alleviate the disease course. Microglia, bone marrow–derived phagocytes and invading lymphocytes (yellow round cells) also partake in the neuroinflammatory condition, which is held to be responsible for dopaminergic neurotoxicity in models of Parkinson's disease. * Figure 2: Loss of CX3CR1 signaling ameliorates amyloid deposition but worsens tau pathology. () In APP/PS-1 mice, which retain CX3CR1, increased levels of Aβ (purple jagged lines) accumulate in plaques surrounded by activated microglia (blue), whose response is modulated by neuronally derived cleaved CX3CL1 (yellow balls) signaling to microglial receptor CX3CR1. () APP/PS-1 mice, which lack CX3CR1, show elevated tissue levels of IL-1, which is associated with enhanced microglial activation (green), improved amyloid clearance, and decreased plaque size and number. () Transgenic mice expressing human tau (hTau) with intact CX3CR1 signaling accumulate hyperphosphorylated tau (red lines) in neuronal somata and dendrites, along with modest microglial activation (blue). MAPT, microtubule-associated protein tau. The straight red lines represent the physiological form of tau, whereas the tangles indicate pathological aggregates of tau. Both are composed of MAPT. () CX3CR1-deficient hTau mice show highly activated microglia (green), which produce large amounts of IL-1, lead! ing to a neuronal response via the IL-1 type 1 receptor, culminating in activated p38 MAP kinase (MAPK) and in tau aggregates (red). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Neuropathology, University Hospital of Freiburg, Freiburg, Germany. * Marco Prinz * Department of Neuropsychiatry and Laboratory of Molecular Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany. * Josef Priller * Department of Neurobiology and Committee on Neurobiology, The University of Chicago, Chicago, Illinois, USA. * Sangram S Sisodia * Neuroinflammation Research Center (Department of Neurosciences, Lerner Research Institute) and Mellen Center for MS Treatment and Research (Neurological Institute), Cleveland Clinic, Cleveland, Ohio, USA. * Richard M Ransohoff Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Richard M Ransohoff or * Marco Prinz Author Details * Marco Prinz Contact Marco Prinz Search for this author in: * NPG journals * PubMed * Google Scholar * Josef Priller Search for this author in: * NPG journals * PubMed * Google Scholar * Sangram S Sisodia Search for this author in: * NPG journals * PubMed * Google Scholar * Richard M Ransohoff Contact Richard M Ransohoff Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (21K) Supplementary Glossary Additional data - Experience-dependent expression of miR-132 regulates ocular dominance plasticity
- Nat Neurosci 14(10):1237-1239 (2011)
Nature Neuroscience | Brief Communication Experience-dependent expression of miR-132 regulates ocular dominance plasticity * Paola Tognini1, 2 * Elena Putignano1, 2 * Alessandro Coatti1, 2 * Tommaso Pizzorusso1, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1237–1239Year published:(2011)DOI:doi:10.1038/nn.2920Received06 May 2011Accepted05 July 2011Published online04 September 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg miR-132 is a CREB-induced microRNA that is involved in dendritic spine plasticity. We found that visual experience regulated histone post-translational modifications at a CRE locus that is important for miR-212 and miR-132 cluster transcription, and regulated miR-132 expression in the visual cortex of juvenile mice. Monocular deprivation reduced miR-132 expression in the cortex contralateral to the deprived eye. Counteracting this miR-132 reduction with an infusion of chemically modified miR-132 mimic oligonucleotides completely blocked ocular dominance plasticity. View full text Figures at a glance * Figure 1: Visual stimulation induces histone mark modifications on specific CRE loci close to the miR-132 coding sequence and activates mature and primary miR-132 expression. () Ac(Lys9-Lys14)H3 ChIP revealed visually induced H3 acetylation at the CRE loci upstream of miR-132, miR-212 and Fos. *P < 0.05. () p(Ser10) Ac(Lys14)H3 ChIP revealed visually induced H3 phosphoacetylation at the CRE sequences upstream of miR-132 and Fos, but not miR-212. () Me2(Lys4)H3 ChIP revealed visually induced H3 dimethylation at the CRE sequence upstream of miR-132 and Fos, but not miR-212. () Developmental expression of pri-miR-132 and miR-132 in the visual cortex of STR and DRB mice. Pri-miR-132 (left) and miR-132 (right) expression significantly increased with age in STR mice, but not in DRB mice. () DR3d decreased pri-miR-132 (left) and miR-132 (right) expression in visual cortex compared with age-matched control mice (P27 STR). () Visual stimulation after DR3d increased pri-miR-132 (left) and miR-132 (right) expression. Data are mean ± s.e.m. * Figure 2: miR-132 downregulation after monocular deprivation regulates ODP in juvenile mice. () We observed decreased p(Ser10) Ac(Lys14)H3 at CRE miR-132 in the binocular visual cortex contralateral to the deprived eye with respect to the ipsilateral cortex in MD3d mice (p(Ser10)Ac(Lys14)H3, right cortex ipsilateral versus left cortex contralateral, n = 13, paired t test, P = 0.015; Ac(Lys9-Lys14)H3, right cortex ipsilateral versus left cortex contralateral, signed rank test, P = 0.3; Me2(Lys4)H3, n = 16, paired t test, P = 0.07). () MD3d decreased pri-miR-132 (left) and miR-132 (right) expression in the binocular visual cortex contralateral to the deprived eye. () ISH for mature miR-132 in MD3d mice revealed a reduction in the cortex contralateral to the deprived eye. Scale bar represents 100 μm. miR-132–positive cell density in nondeprived mice (shaded area, mean ± s.e.m., n = 4) was not different from that of MD3d mice (t test, P > 0.05). In –, error bars represent s.e.m.; *P < 0.05. () Cumulative distribution of ODS in nondeprived (n = 6 mice and 103 cells! ), MD3d (n = 5 mice and 83 cells), MD3d treated with miR-132 mimic (n = 5 mice and 79 cells), and MD3d treated with control miRNA (control miRNA) mice (n = 5 mice and 95 cells). () CBI of nondeprived (n = 6), MD3d (n = 5), MD3d miR-132 mimic (n = 5) and MD3d control miRNA (n = 5) mice. Open circles indicate data points from a single mouse; black circles indicate mean CBI ± s.e.m. () Contralateral-to-ipsilateral VEP ratio of nondeprived (n = 5), MD3d (n = 4), MD3d miR-132 mimic (n = 5) and MD3d control miRNA (n = 5) mice. Open circles indicate data points from a single mouse; black circles indicate average ratio ± s.e.m. Author information * Author information * Supplementary information Affiliations * Istituto di Neuroscienze Consiglio Nazionale delle Ricerche, Pisa, Italy. * Paola Tognini, * Elena Putignano, * Alessandro Coatti & * Tommaso Pizzorusso * Scuola Normale Superiore, Pisa, Italy. * Paola Tognini, * Elena Putignano & * Alessandro Coatti * Dipartimento di Psicologia, Università di Firenze, Firenze, Italy. * Tommaso Pizzorusso Contributions P.T. performed all of the experiments and wrote the manuscript. E.P. performed electrophysiology, ChIP and western blots. A.C. performed ChIP. T.P. wrote the manuscript and supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Tommaso Pizzorusso Author Details * Paola Tognini Search for this author in: * NPG journals * PubMed * Google Scholar * Elena Putignano Search for this author in: * NPG journals * PubMed * Google Scholar * Alessandro Coatti Search for this author in: * NPG journals * PubMed * Google Scholar * Tommaso Pizzorusso Contact Tommaso Pizzorusso Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (524K) Supplementary Figures 1–10 and Methods Additional data - miR-132, an experience-dependent microRNA, is essential for visual cortex plasticity
- Nat Neurosci 14(10):1240-1242 (2011)
Nature Neuroscience | Brief Communication miR-132, an experience-dependent microRNA, is essential for visual cortex plasticity * Nikolaos Mellios1, 5 * Hiroki Sugihara1, 5 * Jorge Castro1 * Abhishek Banerjee1 * Chuong Le1 * Arooshi Kumar1 * Benjamin Crawford1 * Julia Strathmann2 * Daniela Tropea1, 4 * Stuart S Levine3 * Dieter Edbauer2 * Mriganka Sur1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1240–1242Year published:(2011)DOI:doi:10.1038/nn.2909Received23 May 2011Accepted06 July 2011Published online04 September 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Using quantitative analyses, we identified microRNAs (miRNAs) that were abundantly expressed in visual cortex and that responded to dark rearing and/or monocular deprivation. The most substantially altered miRNA, miR-132, was rapidly upregulated after eye opening and was delayed by dark rearing. In vivo inhibition of miR-132 in mice prevented ocular dominance plasticity in identified neurons following monocular deprivation and affected the maturation of dendritic spines, demonstrating its critical role in the plasticity of visual cortex circuits. View full text Figures at a glance * Figure 1: Experience-dependent miRNAs in mouse visual cortex and detailed analysis of miR-132. () miRNA expression, determined by qRT-PCR, in dark rearing (DR) and monocular deprivation (MD) conditions. Data are presented as mean ± s.e.m. relative to control. *P < 0.05, **P < 0.01, two-tailed one-sample Student's t test. () Relative expression of mature miRNA (mean ± s.e.m., normalized to P28 levels), determined by qRT-PCR, during development of V1, as well as after different durations of light exposure in dark-reared mice. ##P < 0.01, ***P < 0.001, ANOVA with Dunnett's test for multiple comparisons relative to control P28. () Representative images from LNA in situ hybridization of miR-132 in V1 at P28 in control, dark rearing and monocular deprivation cases both contralateral (MDc) and ipsilateral (MDi) to the sutured eye. () LNA in situ hybridization for miR-132 in the medial edge of contralateral V1 (marked by the dotted line). WM, white matter. * Figure 2: Structural and electrophysiological effects of miR-132 inhibition. (–) Expression of p250GAP () and mCherry (), as well as their colocalization () in mCherry–miR-132 sponge–expressing neurons. DAPI staining is seen in blue in (). Scale bars represent 25 μm. (,) Confocal microscope images of layer 5 mCherry–miR-132 sponge–expressing neurons in GFP-S transgenic mice. mCherry expression is shown in , and its colocalization with GFP is shown in . The white arrow indicates a mCherry and GFP double-positive neuron that was used for structural analysis. Scale bar represents 50 μm. (,) Representative confocal images from secondary dendritic branches of control () and miR-132 sponge–infected () layer 5 pyramidal neurons used for spine analysis. Scale bar represents 5 μm. (,) Dendritic arborization (Sholl analysis, ) and spine morphology () of sponge-infected and control neurons. Data are presented as mean ± s.e.m. **P < 0.01, two-tailed Student's t test. () Top, representative traces showing mEPSCs recorded from miR-132 sponge–infe! cted and non-infected cells (black and gray, respectively). Scale bars denote 20 pA, 0.5 s. Bottom, amplitude and frequency of mEPSCs recorded from control non-infected (gray) and infected neurons (black, n = 9 neurons per group). Data are presented as mean ± s.e.m., *P < 0.05, two-tailed one-sample Student's t test. * Figure 3: In vivo inhibition of miR-132 in V1 neurons disrupts their ocular dominance plasticity. () Top left, schematic of experimental design. Top right, confocal microscopy images showing mCherry expression in cortical layers 1–3 at P28 following neonatal injection (arrow) of mCherry–miR-132 sponge–expressing lentivirus. Bottom, two-photon microscopy image showing mCherry expression alone (left) and its overlay with OGB (green) fluorescence (right) in an injected mouse that underwent monocular deprivation; selected neurons used in are circled and numbered. Scale bars represent 40 μm. () Example of contralateral eye– and ipsilateral eye–driven calcium responses of the neurons shown in that express high (neuron 1) or low (neuron 2) levels of mCherry–miR-132 sponge. Pink shaded area indicates period with visual stimulus. Calculated ODIs were 0.73 (neuron 1) and 0.55 (neuron 2). () ODI values (ODI = (contralateral – ipsilateral) / (contralateral + ipsilateral); mean ± s.e.m.) derived from peak visual responses obtained by two-photon calcium imaging in mice! injected with mCherry–miR-132 sponge–expressing lentivirus. Black bars indicate mice subjected to 4 d of monocular deprivation and light gray bars indicate mice that were not subjected to monocular deprivation (miR-132 sponge non–monocular deprivation, five mice, 290 neurons; miR-132 sponge monocular deprivation, four mice, 232 neurons; control non–monocular deprivation; three mice, 120 neurons; control monocular deprivation, three mice, 177 neurons). The ODI did not shift following monocular deprivation in mice that were injected with miR-132 sponge, but not control, virus. A significantly larger ocular dominance shift was seen in control monocularly deprived mice than in any of the other conditions (**P < 0.01, Mann-Whitney test comparing neurons; P < 0.05 treating each mouse as a single datum). () Cumulative histogram of ODI data shown in . There was a significant difference between control monocular deprivation and non–monocular deprivation neurons (black and! light gray dashed lines, respectively), but not between miR-1! 32 sponge–infected monocular deprivation and non–monocular deprivation neurons (black and light gray lines, respectively) (Kolmogorov-Smirnov test). Accession codes * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE31536 Author information * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Nikolaos Mellios & * Hiroki Sugihara Affiliations * Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Nikolaos Mellios, * Hiroki Sugihara, * Jorge Castro, * Abhishek Banerjee, * Chuong Le, * Arooshi Kumar, * Benjamin Crawford, * Daniela Tropea & * Mriganka Sur * DZNE - German Center for Neurodegenerative Diseases, Munich, Germany. * Julia Strathmann & * Dieter Edbauer * Biomicro Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. * Stuart S Levine * Present address: Trinity College Dublin, St. James Hospital, Dublin, Ireland. * Daniela Tropea Contributions N.M. conceived the hypothesis, designed and executed experiments, analyzed the data, and wrote the manuscript. H.S. conducted in vivo two-photon calcium imaging and analyzed the related data. J.C. conducted neonatal virus injections and structural analysis. A.B. carried out slice electrophysiology. A.B., C.L., A.K., B.C., J.C. and D.T. assisted in various experiments, data analysis and figure preparation. J.S. and D.E. constructed and tested lentivirus vectors and S.S.L. performed miRNA microarray experiments. M.S. supervised and orchestrated all of the experiments and wrote the manuscript with N.M. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Mriganka Sur Author Details * Nikolaos Mellios Search for this author in: * NPG journals * PubMed * Google Scholar * Hiroki Sugihara Search for this author in: * NPG journals * PubMed * Google Scholar * Jorge Castro Search for this author in: * NPG journals * PubMed * Google Scholar * Abhishek Banerjee Search for this author in: * NPG journals * PubMed * Google Scholar * Chuong Le Search for this author in: * NPG journals * PubMed * Google Scholar * Arooshi Kumar Search for this author in: * NPG journals * PubMed * Google Scholar * Benjamin Crawford Search for this author in: * NPG journals * PubMed * Google Scholar * Julia Strathmann Search for this author in: * NPG journals * PubMed * Google Scholar * Daniela Tropea Search for this author in: * NPG journals * PubMed * Google Scholar * Stuart S Levine Search for this author in: * NPG journals * PubMed * Google Scholar * Dieter Edbauer Search for this author in: * NPG journals * PubMed * Google Scholar * Mriganka Sur Contact Mriganka Sur Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Accession codes * Author information * Supplementary information PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–12, Tables 1–3, Methods and Discussion Additional data - Automatic spread of attentional response modulation along Gestalt criteria in primary visual cortex
- Nat Neurosci 14(10):1243-1244 (2011)
Nature Neuroscience | Brief Communication Automatic spread of attentional response modulation along Gestalt criteria in primary visual cortex * Aurel Wannig1, 3 * Liviu Stanisor1, 3 * Pieter R Roelfsema1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1243–1244Year published:(2011)DOI:doi:10.1038/nn.2910Received31 January 2011Accepted21 June 2011Published online18 September 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Visual attention can select spatial locations, features and objects. Theories of object-based attention claim that attention enhances the representation of all parts of an object, even parts that are not task relevant. We recorded neuronal activity in area V1 of macaque monkeys and observed an automatic spread of attention to image elements outside of the attentional focus when they were bound to an attended stimulus by Gestalt criteria. View full text Figures at a glance * Figure 1: The effect of collinearity on the spread of attention. () Schematic sequence of stimulus and behavioral events during a trial. The monkey foveated a fixation point (FP). After 300 ms, an array of four bars appeared, and after 500 ms, a saccade target (ST) dot appeared over one of the more central bars. The fixation point disappeared after an additional 500 ms and the monkey made an eye movement toward the saccade target (green arrow). Neuronal responses were simultaneously recorded from two recording sites; receptive fields (RF1 and RF2) are shown as squares. () The multi-unit activity (MUA) of neurons at site 1 increased when the saccade target appeared in the receptive field, at a latency of 44 ms (red curve), but not if it appeared on the other bar (blue). () Neuronal responses at recording site 2. Cueing of the lower target bar, which is grouped to the receptive field bar, caused a stronger response than cueing of the upper target bar, at a latency of 328 ms. () Cueing of the central bars had little influence when they were ! orthogonal to the receptive field bar. Ethical permission was obtained from the institutional animal care and use committee of the Royal Netherlands Academy of Arts and Sciences. * Figure 2: Effects of Gestalt cues on the spread of enhanced activity at the population level. () The influence of collinearity. () Effect of color similarity. () Combined effect of collinearity and color similarity. () Effect of common fate. Histograms depict the average differences in activity between cueing conditions (see insets) for grouped (light gray bars) and nongrouped configurations (black bars). Error bars denote s.e.m. Green rectangles illustrate the superimposed (and scaled) locations of the receptive fields of all recording sites relative to the stimuli, indicating that the neurons were not directly activated by the surrounding stimuli. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Aurel Wannig & * Liviu Stanisor Affiliations * Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands. * Aurel Wannig, * Liviu Stanisor & * Pieter R Roelfsema * Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands. * Pieter R Roelfsema Contributions A.W. designed the stimuli, performed recordings, analyzed data and wrote the paper. L.S. designed the stimuli and performed recordings. P.R.R. conceived the project, supervised the data acquisition and analysis, and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Pieter R Roelfsema Author Details * Aurel Wannig Search for this author in: * NPG journals * PubMed * Google Scholar * Liviu Stanisor Search for this author in: * NPG journals * PubMed * Google Scholar * Pieter R Roelfsema Contact Pieter R Roelfsema Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (688K) Supplementary Figures 1–6, Supplementary Results, Supplementary Methods and Supplementary Discussion Additional data - A lateralized brain network for visuospatial attention
- Nat Neurosci 14(10):1245-1246 (2011)
Nature Neuroscience | Brief Communication A lateralized brain network for visuospatial attention * Michel Thiebaut de Schotten1, 2, 3, 7 * Flavio Dell'Acqua1, 3, 4, 7 * Stephanie J Forkel1 * Andrew Simmons3, 4, 5 * Francesco Vergani6 * Declan G M Murphy1 * Marco Catani1, 3 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1245–1246Year published:(2011)DOI:doi:10.1038/nn.2905Received11 April 2011Accepted07 July 2011Published online18 September 2011 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Right hemisphere dominance for visuospatial attention is characteristic of most humans, but its anatomical basis remains unknown. We report the first evidence in humans for a larger parieto-frontal network in the right than left hemisphere, and a significant correlation between the degree of anatomical lateralization and asymmetry of performance on visuospatial tasks. Our results suggest that hemispheric specialization is associated with an unbalanced speed of visuospatial processing. View full text Figures at a glance * Figure 1: The three branches of the superior longitudinal fasciculus (SLF I, II and III). (,) Comparison between axonal tracing in monkey6, 10 () and in vivo spherical deconvolution (SD) tractography in humans (). Three-dimensional reconstructions are displayed at the top of each panel, and coronal sections at the indicated y planes are at the bottom. * Figure 2: Correlations between anatomical and behavioral lateralizations. () Hemispheric lateralization of the three SLF branches, with 95% confidence intervals. (,) Correlations between the lateralization of the SLF II and both the deviation on the line bisection task () and the lateralization of the detection time (). () Correlation between the deviation on the line bisection task and the detection time. *P < 0.05 and ***P < 0.001. Author information * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Michel Thiebaut de Schotten & * Flavio Dell'Acqua Affiliations * Natbrainlab, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, King's College London, London, UK. * Michel Thiebaut de Schotten, * Flavio Dell'Acqua, * Stephanie J Forkel, * Declan G M Murphy & * Marco Catani * INSERM–Université Pierre et Marie Curie (UPMC) Unité Mixte de Recherche (UMR) Groupe Hospitalier (GH) Pitié–Salpêtrière, Paris, France. * Michel Thiebaut de Schotten * Department of Neuroimaging, Institute of Psychiatry, King's College London, London, UK. * Michel Thiebaut de Schotten, * Flavio Dell'Acqua, * Andrew Simmons & * Marco Catani * National Institute for Health Research Biomedical Research Centre for Mental Health, London, UK. * Flavio Dell'Acqua & * Andrew Simmons * Medical Research Council Centre for Neurodegeneration Research, King's College London, London, UK. * Andrew Simmons * Department of Neurosurgery, Royal Victoria Infirmary, Newcastle upon Tyne, UK. * Francesco Vergani Contributions M.T.d.S. conceived and coordinated the study, reviewed and collected neuropsychological data, performed the tractography dissections, helped with the post-mortem dissections and wrote the manuscript. F.D. developed the spherical deconvolution algorithm, collected and preprocessed the neuroimaging data before the dissections and helped drafting the manuscript. S.J.F. helped collecting neuropsychological data and drafting the manuscript. A.S. and D.G.M.M. provided funding for the neuroimaging data and helped to draft the manuscript. F.V. helped drafting the manuscript and performed the post-mortem dissections. M.C. helped to conceive and coordinate the study. M.C. also wrote the manuscript and performed the post-mortem dissections. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Michel Thiebaut de Schotten Author Details * Michel Thiebaut de Schotten Contact Michel Thiebaut de Schotten Search for this author in: * NPG journals * PubMed * Google Scholar * Flavio Dell'Acqua Search for this author in: * NPG journals * PubMed * Google Scholar * Stephanie J Forkel Search for this author in: * NPG journals * PubMed * Google Scholar * Andrew Simmons Search for this author in: * NPG journals * PubMed * Google Scholar * Francesco Vergani Search for this author in: * NPG journals * PubMed * Google Scholar * Declan G M Murphy Search for this author in: * NPG journals * PubMed * Google Scholar * Marco Catani 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 1–8, Supplementary Tables 1 and 2, Supplementary Methods, Supplementary Results and Supplementary Note Additional data - A category-specific response to animals in the right human amygdala
- Nat Neurosci 14(10):1247-1249 (2011)
Nature Neuroscience | Brief Communication A category-specific response to animals in the right human amygdala * Florian Mormann1, 2, 3 * Julien Dubois1 * Simon Kornblith1 * Milica Milosavljevic1 * Moran Cerf1, 2 * Matias Ison2, 4 * Naotsugu Tsuchiya1 * Alexander Kraskov1, 5 * Rodrigo Quian Quiroga1, 4 * Ralph Adolphs1 * Itzhak Fried2, 6 * Christof Koch1, 7 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1247–1249Year published:(2011)DOI:doi:10.1038/nn.2899Received25 February 2011Accepted28 June 2011Published online28 August 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 amygdala is important in emotion, but it remains unknown whether it is specialized for certain stimulus categories. We analyzed responses recorded from 489 single neurons in the amygdalae of 41 neurosurgical patients and found a categorical selectivity for pictures of animals in the right amygdala. This selectivity appeared to be independent of emotional valence or arousal and may reflect the importance that animals held throughout our evolutionary past. View full text Figures at a glance * Figure 1: Amygdala neurons respond preferentially to animal pictures. () Response probabilities of neurons in different MTL regions to different stimulus categories revealed significant preferences in the amygdala (P < 10−15, main effect of increased responses to animals at ~1%) and entorhinal cortex (P < 0.03, main effect of decreased responses to persons), but not in the hippocampus. () Mean response magnitudes of all responsive neurons showed increased response activity of amygdala neurons to animals (P < 10−5). (,) The animal preference in both response probability and magnitude was seen only in the right amygdala (P < 10−15 and P < 0.0005, respectively). Error bars denote binomial 68% confidence intervals (,) and s.e.m. (,). *P < 0.05, ***P < 0.001. * Figure 2: A specific category response to animals in the right amygdala at the population level. () For a set of 201 amygdala units (96 left, 105 right) that were all presented with the same 57 stimuli (23 persons, 16 animals, 18 landmarks), we constructed representational dissimilarity matrices by determining the dissimilarity in evoked response patterns for each pair of stimuli (as 1 – r from the Pearson correlation across units). () Hierarchical cluster analysis automatically grouped stimuli with similar response patterns together into clusters. In the right amygdala, this unsupervised procedure yielded a cluster that contained all animal stimuli, whereas no such category effect was found in the left amygdala. Author information * Author information * Supplementary information Affiliations * Division of Biology, California Institute of Technology, Pasadena, California, USA. * Florian Mormann, * Julien Dubois, * Simon Kornblith, * Milica Milosavljevic, * Moran Cerf, * Naotsugu Tsuchiya, * Alexander Kraskov, * Rodrigo Quian Quiroga, * Ralph Adolphs & * Christof Koch * Department of Neurosurgery and Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, USA. * Florian Mormann, * Moran Cerf, * Matias Ison & * Itzhak Fried * Department of Epileptology, University of Bonn, Bonn, Germany. * Florian Mormann * Department of Engineering, University of Leicester, Leicester, UK. * Matias Ison & * Rodrigo Quian Quiroga * University College London Institute of Neurology, London, UK. * Alexander Kraskov * Functional Neurosurgery Unit, Tel-Aviv Medical Center and Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel. * Itzhak Fried * Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea. * Christof Koch Contributions F.M., S.K., R.Q.Q., I.F. and C.K. designed the electrophysiology study. I.F. carried out all of the neurosurgical procedures. F.M., M.C., M.I., R.Q.Q., A.K. and I.F. collected the electrophysiological data, and S.K. and F.M. analyzed the electrophysiological data. F.M., N.T., M.M., C.K. and R.A. designed the fMRI control experiment, F.M., M.M., J.D. and N.T. collected the fMRI data, and J.D. and F.M. analyzed the fMRI data. F.M., R.A. and C.K. wrote the paper. All of the authors discussed the results and commented on the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Florian Mormann Author Details * Florian Mormann Contact Florian Mormann Search for this author in: * NPG journals * PubMed * Google Scholar * Julien Dubois Search for this author in: * NPG journals * PubMed * Google Scholar * Simon Kornblith Search for this author in: * NPG journals * PubMed * Google Scholar * Milica Milosavljevic Search for this author in: * NPG journals * PubMed * Google Scholar * Moran Cerf Search for this author in: * NPG journals * PubMed * Google Scholar * Matias Ison Search for this author in: * NPG journals * PubMed * Google Scholar * Naotsugu Tsuchiya Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Kraskov Search for this author in: * NPG journals * PubMed * Google Scholar * Rodrigo Quian Quiroga Search for this author in: * NPG journals * PubMed * Google Scholar * Ralph Adolphs Search for this author in: * NPG journals * PubMed * Google Scholar * Itzhak Fried Search for this author in: * NPG journals * PubMed * Google Scholar * Christof Koch 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 1–12, Supplementary Methods, Supplementary Results, Supplementary Control Analyses and SupSupplementary Discussion Additional data - Differential roles of human striatum and amygdala in associative learning
- Nat Neurosci 14(10):1250-1252 (2011)
Nature Neuroscience | Brief Communication Differential roles of human striatum and amygdala in associative learning * Jian Li1, 2 * Daniela Schiller3 * Geoffrey Schoenbaum4, 5, 6 * Elizabeth A Phelps1, 2 * Nathaniel D Daw1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1250–1252Year published:(2011)DOI:doi:10.1038/nn.2904Received12 April 2011Accepted07 July 2011Published online11 September 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 the human amygdala and striatum have both been implicated in associative learning, only the striatum's contribution has been consistently computationally characterized. Using a reversal learning task, we found that amygdala blood oxygen level–dependent activity tracked associability as estimated by a computational model, and dissociated it from the striatal representation of reinforcement prediction error. These results extend the computational learning approach from striatum to amygdala, demonstrating their complementary roles in aversive learning. View full text Figures at a glance * Figure 1: Experimental design and behavioral model fit. () Experiment timeline illustration. The acquisition phase consisted of presentations of the conditioned stimulus (CS+), which was partially associated with electric shock, and an unconditioned stimulus (CS−) that was not associated with shock. In the reversal phase, the reinforcement contingencies for the original conditioned and unconditioned stimuli were switched. () Average SCRs across subjects (red) and the best-fit associability trace (blue). * Figure 2: Neural correlates of associability and prediction error term. () BOLD activity in the ventral striatum, but not in the amygdala, correlated with prediction error. () BOLD activity in the bilateral amygdala, but not in the ventral striatum, correlated with associability regressor (P < 0.05, SVC). The results are shown at uncorrected thresholds to display the full extent of the activation. () Differential representations of associability (α) and prediction error (δ) in striatum and amygdala BOLD activity (±s.e.m.) plotted as regression effect sizes (β values, arbitrary units). Author information * Author information * Supplementary information Affiliations * Department of Psychology, New York University, New York, New York, USA. * Jian Li, * Elizabeth A Phelps & * Nathaniel D Daw * Center for Neural Science, New York University, New York, New York, USA. * Jian Li, * Elizabeth A Phelps & * Nathaniel D Daw * Departments of Psychiatry and Neuroscience, and Friedman Brain Institute, Mt. Sinai School of Medicine, New York, New York, USA. * Daniela Schiller * Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, Maryland, USA. * Geoffrey Schoenbaum * Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA. * Geoffrey Schoenbaum * National Institute on Drug Abuse Intramural Research Program, Baltimore, Maryland, USA. * Geoffrey Schoenbaum Contributions E.A.P. and D.S. designed the study and conducted the experiment. J.L. and N.D.D. performed the data analysis. J.L., D.S., G.S., E.A.P. and N.D.D. interpreted the data and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jian Li Author Details * Jian Li Contact Jian Li Search for this author in: * NPG journals * PubMed * Google Scholar * Daniela Schiller Search for this author in: * NPG journals * PubMed * Google Scholar * Geoffrey Schoenbaum Search for this author in: * NPG journals * PubMed * Google Scholar * Elizabeth A Phelps Search for this author in: * NPG journals * PubMed * Google Scholar * Nathaniel D Daw 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 1–3, Supplementary Tables 1–5 and Supplementary Methods Additional data - Ligand-binding domain of an α7-nicotinic receptor chimera and its complex with agonist
- Nat Neurosci 14(10):1253-1259 (2011)
Nature Neuroscience | Article Ligand-binding domain of an α7-nicotinic receptor chimera and its complex with agonist * Shu-Xing Li1, 5 * Sun Huang2, 5 * Nina Bren2, 5 * Kaori Noridomi1 * Cosma D Dellisanti1 * Steven M Sine2, 3 * Lin Chen1, 4 * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:1253–1259Year published:(2011)DOI:doi:10.1038/nn.2908Received24 May 2011Accepted20 July 2011Published online11 September 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 The α7 acetylcholine receptor (AChR) mediates pre- and postsynaptic neurotransmission in the central nervous system and is a potential therapeutic target in neurodegenerative, neuropsychiatric and inflammatory disorders. We determined the crystal structure of the extracellular domain of a receptor chimera constructed from the human α7 AChR and Lymnaea stagnalis acetylcholine binding protein (AChBP), which shares 64% sequence identity and 71% similarity with native α7. We also determined the structure with bound epibatidine, a potent AChR agonist. Comparison of the structures revealed molecular rearrangements and interactions that mediate agonist recognition and early steps in signal transduction in α7 AChRs. The structures further revealed a ring of negative charge within the central vestibule, poised to contribute to cation selectivity. Structure-guided mutational studies disclosed distinctive contributions to agonist recognition and signal transduction in α7 AChRs. Th! e structures provide a realistic template for structure-aided drug design and for defining structure–function relationships of α7 AChRs. View full text Figures at a glance * Figure 1: Sequence and numbering of the α7–AChBP chimera and its alignment with related AChR sequences. Orange indicates invariant residues and yellow indicates partially conserved residues. Secondary structures are shown schematically above the sequences. Putative functionally important residues for ligand recognition (pink), signal transduction (blue) and inorganic ion binding (red) are shown. Loops F and C are indicated by green bars. * Figure 2: Overall structures of the α7–AChBP chimera and comparison to related structures. () Top view of the α7–AChBP chimera pentamer along the five-fold axis of symmetry; each subunit is shown in a different color. () Structure superposition between the α7–AChBP chimera (blue) and AChBP (orange) pentamers viewed from the side that is normal to the five-fold axis. () Structure superposition of subunits from the α7–AChBP chimera (blue), α1 extracellular domain (magenta) and AChBP (orange); loops showing substantial differences are labeled. () Surface representation showing α7 residues (blue) and AChBP residues area (beige) on the α7–AChBP chimera. () Backbone superposition between the Apo (gold) and Epi (blue) structures viewed down the five-fold axis. The epibatidine molecule (Epi) is shown by the Fo – Fc electron density contoured at the 3.0-σ level. * Figure 3: Structures specific to α7 revealed by the α7–AChBP chimera. () Four regions of α7-specific residues near loops C (magenta) and F (red), indicated by I–IV. () Close-up of the signal transduction region beneath loop C. Alternative conformations of Arg182 are indicated by different colors. () Close-up of linkage region between loops C and F within the same subunit. () Glu185-Glu158-Asp160 triad spanning loop C of the principal subunit and loop F of the complementary subunit, in ribbon () and surface () representation. Positive and negative surface potentials are indicated by blue and red, respectively. () Close-up of glycan across from loop C. NAG, N-acetylglucosamine. * Figure 4: Epibatidine-induced conformational changes. () Backbone superposition between the Apo (gold) and Epi (blue) structures shows a clockwise rotation of the outer β-sheet (green box and arrow, bottom) and a counterclockwise rotation of the top part of the subunit structure (red box and arrow, top) when viewed down the pentamer axis. The stationary inner sheet is indicated by the black box, and the epibatidine molecule is shown in electron density. The side chain rotamer switch of Phe196 is also evident in the green box. () Backbone superposition of individual subunits show variable conformations of loop C in the Apo structure (ten subunits colored differently) but a single closed conformation in the Epi structure (black, only one structure shown). Epi indicates the epibatidine molecule. () The 2Fo – Fc electron density map (contoured at the 1.0-σ level) shows the distinct side chain conformations of Phe196 (arrows) in the Apo (left) and Epi (right) structures, demonstrating repacking of the protein core as a result of! epibatidine-induced structural changes. * Figure 5: Epibatidine-induced structural reorganization of the ligand-binding pocket and flanking regions. () Comparison of the ligand-binding pocket between the Apo (gold) and Epi (blue) structures. () Comparison of key residues underneath loop C implicated in signal transduction. () Highly ordered assembly of Tyr184, Tyr91, Lys141, Arg182 and a solvent molecule in the Epi structure. Epi indicates the epibatidine molecule. () Comparison of the interactions at the tip of loop C between the Apo (gold) and Epi (blue) structures. NAG, N-acetylglucosamine. * Figure 6: Molecular recognition of epibatidine. () Stereo view of the ligand-binding pocket from the side of the pentamer showing the position of epibatidine (Epi) in the aromatic cage. The protein is in ribbon style and the epibatidine molecule is shown with the Fo – Fc electron density contoured at the 3.0-σ level. () Stereo view of the ligand-binding pocket from above the pentamer. This view highlights hydrogen-bond interactions and interactions with the complementary face of the binding site between epibatidine and the receptor chimera. * Figure 7: Pore-facing regions of inter-subunit contact. () Bottom view of the pentamer along the five-fold axis of symmetry, showing the tandem arrangement of the β1–β2 loops and the ring of ten aspartate and five asparagine residues. () Inter-subunit contacts between the tip of the β1–β2 loop of the principal subunit (blue) and the stem of the β1–β2 loop of the complementary subunit (orange). * Figure 8: Agonist binding after mutation of key residues. () Ligand contact residues. () Non-contact residues. Binding of epibatidine and ACh to native α7 AChRs was measured by competition against the initial rate of 125I-labeled α-bungarotoxin binding (see Online Methods). Curves are nonlinear least-squares fits of the Hill equation to the data with fit parameters given in Supplementary Table 2. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions GenBank * X70297 Protein Data Bank * 3SQ9 * 3SQ6 * 3SQ9 * 3SQ6 Author information * Abstract * Accession codes * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Shu-Xing Li, * Sun Huang & * Nina Bren Affiliations * Molecular and Computational Biology, Departments of Biological Sciences and Chemistry, University of Southern California, Los Angeles, California, USA. * Shu-Xing Li, * Kaori Noridomi, * Cosma D Dellisanti & * Lin Chen * Department of Physiology and Biomedical Engineering Mayo Clinic College of Medicine, Rochester, Minnesota, USA. * Sun Huang, * Nina Bren & * Steven M Sine * Department of Neurology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA. * Steven M Sine * Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. * Lin Chen Contributions S.M.S. and L.C. supervised the project; S.H., N.B. and S.M.S. designed and built the α7–AChBP chimera; N.B. and S.H. expressed the protein; S.H., N.B. and S.-X.L. purified the protein; S.-X.L., S.H., C.D.D., K.N. and L.C. grew the crystals; S.-X.L. and L.C. collected diffraction data, solved and refined the structure; S.M.S. and N.B. conducted the radioligand binding experiments; and S.M.S., L.C., S.L. and S.H. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Lin Chen or * Steven M Sine Author Details * Shu-Xing Li Search for this author in: * NPG journals * PubMed * Google Scholar * Sun Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Nina Bren Search for this author in: * NPG journals * PubMed * Google Scholar * Kaori Noridomi Search for this author in: * NPG journals * PubMed * Google Scholar * Cosma D Dellisanti Search for this author in: * NPG journals * PubMed * Google Scholar * Steven M Sine Contact Steven M Sine Search for this author in: * NPG journals * PubMed * Google Scholar * Lin Chen Contact Lin Chen 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 (1M) Supplementary Figures 1–11, Supplementary Tables 1 and 2 Additional data - Engrailed protects mouse midbrain dopaminergic neurons against mitochondrial complex I insults
- Nat Neurosci 14(10):1260-1266 (2011)
Nature Neuroscience | Article Engrailed protects mouse midbrain dopaminergic neurons against mitochondrial complex I insults * Daniel Alvarez-Fischer1, 2, 3, 4, 10 * Julia Fuchs2, 5, 6, 10 * François Castagner2, 5, 6 * Olivier Stettler2, 5, 7 * Olivia Massiani-Beaudoin2, 5, 6 * Kenneth L Moya2, 5, 6 * Colette Bouillot2, 5, 6 * Wolfgang H Oertel4 * Anne Lombès8 * Wolfgang Faigle9 * Rajiv L Joshi2, 5, 6 * Andreas Hartmann1, 3 * Alain Prochiantz2, 5, 6 * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:1260–1266Year published:(2011)DOI:doi:10.1038/nn.2916Received16 June 2011Accepted18 July 2011Published online04 September 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 Mice heterozygous for the homeobox gene Engrailed-1 (En1) display progressive loss of mesencephalic dopaminergic (mDA) neurons. We report that exogenous Engrailed-1 and Engrailed-2 (collectively Engrailed) protect mDA neurons from 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), a mitochondrial complex I toxin used to model Parkinson's disease in animals. Engrailed enhances the translation of nuclearly encoded mRNAs for two key complex I subunits, Ndufs1 and Ndufs3, and increases complex I activity. Accordingly, in vivo protection against MPTP by Engrailed is antagonized by Ndufs1 small interfering RNA. An association between Engrailed and complex I is further confirmed by the reduced expression of Ndufs1 and Ndufs3 in the substantia nigra pars compacta of En1 heterozygous mice. Engrailed also confers in vivo protection against 6-hydroxydopamine and α-synuclein-A30P. Finally, the unilateral infusion of Engrailed into the midbrain increases striatal dopamine content, res! ulting in contralateral amphetamine-induced turning. Therefore, Engrailed is both a survival factor for adult mDA neurons and a regulator of their physiological activity. View full text Figures at a glance * Figure 1: Engrailed protects against complex I, but not against complex II, inhibition. () Engrailed protects cultured embryonic midbrain tyrosine hydroxylase–positive (TH+) neurons against MPP+. En1 or En2 (3 nM), but not an uninternalized mutant form of En2, EnSR, doubled the survival of TH+ cells after MPP+ (3 μM) treatment. Data are expressed as a percentage of the vehicle value (mean ± s.e.m.), which was 2,003 ± 110 TH+ cells per well. *P < 0.05; n = 5–10. () Dose-dependent protection of Engrailed against MPP+, with maximal protective effects between 0.3 and 3 nM. Data are expressed as a percentage of the MPP+ treatment value (mean ± s.e.m.). *P < 0.05; n = 5–6. () The total number of cultured neurons (NeuN+ cells), 277,000 ± 3,450 (set at 100%) is not modified by MPP+ and/or Engrailed treatments. () Engrailed fully protects TH+ and the total neurons (NeuN+) against rotenone (Rot), an unspecific complex I inhibitor. Rotenone treatment (50 nM, days 4–5) led to a 40% (mean ± s.e.m.) loss of TH+ cells () and 18% loss of NeuN+ cells (). *P < 0.05! ; n = 5–7. () The addition of complex II inhibitor 3-NP (100 μM) between days 4 and 6 in vitro leads to a 36.6% ± 1.9% (mean ± s.e.m.) reduction of TH+ neurons () and to a 28.8% ± 1.9% reduction of NeuN+ neurons (), without any protective effect of Engrailed. Data are expressed as percentage of control, which corresponds to about 2,000 TH+ and 270,000 NeuN+ cells per well, respectively. NS, not significant; *P < 0.001; n = 7–13. * Figure 2: Engrailed increases the translation of specific complex I subunits and complex I activity. () Metabolic labeling of midbrain synaptoneurosomes followed by complex I immunocapture, SDS-PAGE analysis and mass spectrometry shows an upregulation by En1 of complex I subunits, including Ndufs1 and Ndufs3. () Densitometric quantification of complex I subunits specifically identified by mass spectrometry and western blotting, comparing the intensity of complex I subunit proteins in synaptoneurosomes treated with En1 or vehicle (control). () Engrailed enhances complex I activity. Complex I activity, but not complex IV activity, was 20% greater in Engrailed-treated synaptoneurosomes. Citrate synthase activity, an index of metabolic activity, was also greater upon Engrailed treatment. Data are expressed as percentage of the vehicle-treated synaptoneurosome value (mean ± s.e.m.), which were an NADH oxidation rate of 188.6 ± 4.7 μmol min−1 per milligram protein for complex I, cytochrome c oxidation rate of 375.8 ± 5.3 μmol min−1 per milligram protein for complex IV an! d acetyl-coenzyme A conversion rate of 1,028.9 ± 13.7 μmol min−1 per milligram protein for citrate synthase, respectively. NS, not significant; *P < 0.05; n = 3–4. * Figure 3: Engrailed enhances Ndufs1 and Ndufs3 levels in primary midbrain neuron cultures. (,) En1 (3 nM) or vehicle (control) was added for 4 h to primary mesencephalic neurons in culture (5 d in vitro), and the steady-state amounts of Ndufs1, Ndufs3 and actin (gel loading control) were estimated by western blotting. Densitometric quantification (mean ± s.e.m.) showed a significant increase in both Ndufs1 () and Ndufs3 () proteins upon En1 treatment. This increase was completely blocked by preincubation of En1 with an antibody to Engrailed (antibody 86/8; ref. 7) and was unaffected by actinomycin D (actD) treatment (5 μg ml−1) sufficient to block transcription (verified by quantitative reverse transcription–polymerase chain reaction; data not shown). () Under the same conditions, the amounts of Cox IV remained unaffected. NS, not significant; *P < 0.05; n = 5. * Figure 4: Ndufs1 and Ndufs3 expression is diminished in En1+/−En2+/+ mice. () Immunohistochemical analysis of Ndufs1 staining in the SNpc. Free-floating cryosections of the entire midbrain of 1-year-old En1+/−En2+/+ mice and WT littermates were stained for Ndufs1 (and Ndufs3, not shown). Neurons in the SNpc of mutant mice showed a decrease in the amount of the proteins. Scale bars, 100 μm. () Quantification of Ndufs1, Ndufs3 and Cox IV staining. Optical density per neuron in the SNpc showed a decrease in Ndufs1 (25.39% ± 5.76%; mean ± s.e.m.) and Ndufs3 (24.05% ± 7.26%) protein. The intensity of Cox IV staining was identical in mutant and WT mice; *P < 0.05; n = 4. Ndufs3 staining intensity analyzed in the nucleus of the oculomotor nerve showed no alteration in En1+/−En2+/+ mice; *P < 0.05; n = 4. () Double-fluorescence immunostaining of Ndufs3 and tyrosine hydroxylase (TH) on free-floating coronal vibratome sections of 1-year-old En1+/−En2+/+ mice and WT littermates (confocal sections). () Ndufs3 staining intensity was 21.26% ± 3.3% low! er (mean ± s.e.m.) in TH+ neurons from En1+/−En2+/+ mice compared to that in neurons from WT mice; *P < 0.01; n = 3. * Figure 5: Engrailed protection against MPTP is Ndufs1 dependent. () Experimental scheme. C57Bl/6 mice were infused with a mixture of En1 and En2 (1:1, 4.5 μM total) for 14 d dorsal to the SNpc. MPTP (30 mg per kilogram body weight) or saline was injected (i.p.) for 5 consecutive days starting on day 5 after pump implantation42, and mice were killed on day 21. () Compared to the sham-infused side, the Engrailed-infused side of MPTP-injected mice shows a partial preservation of tyrosine hydroxylase–positive (TH+) neurons in the SNpc (peroxidase immunostaining). Scale bar, 100 μm. () Representative images of the ipsilateral (Engrailed- plus siRNA-infused side) and the contralateral (uninfused) side of the same mouse. () MPTP decreased the number of TH+ cells by 32.1% ± 3.1% (mean ± s.e.m.) on sham-infused and contralateral side (*P < 0.01). The infusion of En1 and En2 reduced cell loss to 15% ± 3.4% (#P < 0.01). The uninfused side of control mice injected with saline was set at 100% of TH+ cells and used for comparison with all other ! conditions. Data are expressed as percentage of control, n = 6–10 mice per group. C, contralateral, uninfused side; S, sham-infused side; En, Engrailed-infused side. The number of TH− neurons was the same in all conditions (Supplementary Fig. 5). () The number of TH+ neurons in the MPTP-treated mice (3,964 ± 300) was 36% ± 3.7% lower. Engrailed protection against MPTP (+26.1%) was abolished by the Ndufs1 siRNA (4,065 ± 414) but not by a control siRNA (4,556 ± 252). Data are expressed as percentage of control. *P < 0.05; n = 4–7. * Figure 6: Engrailed-driven increases in striatal dopamine and turning behavior after MPTP are Ndufs1 dependent. () MPTP induced a 46% ± 2.3% (mean ± s.e.m.) decrease in striatal dopamine (DA) content compared to unlesioned mice. Engrailed infusion fully antagonized this decrease (115.25% ± 19.23%). Saline-injected control mice infused with Engrailed showed a 77.5% ± 20.42% increase in striatal dopamine compared to baseline (saline-infused mice and the uninfused contralateral side of the same mice). C, contralateral side; S, sham-infused; En, Engrailed-infused. *P < 0.05; #P < 0.05; n = 4–8. () In conditions where MPTP decreased DA 90%, Engrailed infusion increased DA by 3.5-fold (35% of saline-injected, sham-infused controls). Protection was fully blocked by the Ndufs1 siRNA but not by a control siRNA (saline-injected, sham-infused controls set as 100%, n = 5–7). () Engrailed induced significant contralateral turning in MPTP-intoxicated mice in the presence of a control siRNA. This behavior was fully antagonized when Engrailed was infused with Ndufs1 siRNA. Data expressed in n! et turns per minute. *P < 0.05, **P < 0.01; n = 5–10. () Engrailed significantly induced contralateral turning in unlesioned amphetamine-treated mice compared to sham-infused mice. ***P < 0.001; n = 17. * Figure 7: Engrailed protects against 6-OHDA and α-synuclein-A30P toxicity. () 6-OHDA decreased the number of tyrosine hydroxylase–positive (TH+) neurons by 59.35% ± 2.54% (mean ± s.e.m.; *P < 0.001) compared to that on the untreated side of each mouse. The infusion of En1 and En2 reduced cell loss to 17.81% ± 6.12% (#P < 0.001). Data are expressed as percentage of control; n = 6–10 mice per group. S, sham-infused mice; En, Engrailed-infused mice. () Treatment with α-synuclein-A30P decreased the number of TH+ neurons by 33.47% ± 9.93% (mean ± s.e.m.; *P = 0.005) compared to that on the untreated side, and infused En fully protected (compared to saline, #P < 0.001). No difference was detectable between the group treated with α-synuclein-A30P plus En and the control group (P = 0.46). Data are expressed in percentage of control; n = 4–6 mice per group. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Daniel Alvarez-Fischer & * Julia Fuchs Affiliations * Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinière University Pierre and Marie Curie/Institut National de la Santé et de la Recherche Médicale, Unité Mixte de Recherche S 975, Paris, France. * Daniel Alvarez-Fischer & * Andreas Hartmann * Collège de France, Center for Interdisciplinary Research in Biology (CIRB)/Centre National de la Recherche Scientifique, Unité Mixte de Recherche 7241/Institut National de la Santé et de la Recherche Médicale U1050, Paris, France. * Daniel Alvarez-Fischer, * Julia Fuchs, * François Castagner, * Olivier Stettler, * Olivia Massiani-Beaudoin, * Kenneth L Moya, * Colette Bouillot, * Rajiv L Joshi & * Alain Prochiantz * Groupe Hospitalier Pitié-Salpêtrière, Paris, France. * Daniel Alvarez-Fischer & * Andreas Hartmann * Department of Neurology, Philipps University, Marburg, Germany. * Daniel Alvarez-Fischer & * Wolfgang H Oertel * Labex Memolife, Paris Sciences Lettres Research University. * Julia Fuchs, * François Castagner, * Olivier Stettler, * Olivia Massiani-Beaudoin, * Kenneth L Moya, * Colette Bouillot, * Rajiv L Joshi & * Alain Prochiantz * University Pierre et Marie Curie, Ecole Doctorale 158, Paris, France. * Julia Fuchs, * François Castagner, * Olivia Massiani-Beaudoin, * Kenneth L Moya, * Colette Bouillot, * Rajiv L Joshi & * Alain Prochiantz * Université Paris Descartes, Paris, France. * Olivier Stettler * Institut National de la Santé et de la Recherche Médicale U975, Hôpital de La Salpêtrière, Paris, France. * Anne Lombès * Laboratoire de Spectrométrie de Masse, Institut Curie, Paris, France. * Wolfgang Faigle Contributions A.P. proposed the initial hypothesis that Engrailed could be used as a protective protein in animal models of Parkinson's disease. D.A.-F., J.F., F.C., O.M.-B. and R.L.J. performed experiments to study Engrailed survival activity. O.S., C.B., K.L.M., J.F., O.M.-B. and R.L.J. examined Engrailed translational targets. W.F. helped with mass spectroscopy. A.L. measured complex I activity. W.H.O. participated in discussions. J.F., D.A.-F., R.L.J., A.H. and A.P. designed the experiments and interpreted the results. D.A.-F., J.F., R.L.J. and A.P. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Alain Prochiantz or * Rajiv L Joshi or * Andreas Hartmann Author Details * Daniel Alvarez-Fischer Search for this author in: * NPG journals * PubMed * Google Scholar * Julia Fuchs Search for this author in: * NPG journals * PubMed * Google Scholar * François Castagner Search for this author in: * NPG journals * PubMed * Google Scholar * Olivier Stettler Search for this author in: * NPG journals * PubMed * Google Scholar * Olivia Massiani-Beaudoin Search for this author in: * NPG journals * PubMed * Google Scholar * Kenneth L Moya Search for this author in: * NPG journals * PubMed * Google Scholar * Colette Bouillot Search for this author in: * NPG journals * PubMed * Google Scholar * Wolfgang H Oertel Search for this author in: * NPG journals * PubMed * Google Scholar * Anne Lombès Search for this author in: * NPG journals * PubMed * Google Scholar * Wolfgang Faigle Search for this author in: * NPG journals * PubMed * Google Scholar * Rajiv L Joshi Contact Rajiv L Joshi Search for this author in: * NPG journals * PubMed * Google Scholar * Andreas Hartmann Contact Andreas Hartmann Search for this author in: * NPG journals * PubMed * Google Scholar * Alain Prochiantz Contact Alain Prochiantz 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 Figures 1–6 Additional data - Drosophila Rae1 controls the abundance of the ubiquitin ligase Highwire in post-mitotic neurons
- Nat Neurosci 14(10):1267-1275 (2011)
Nature Neuroscience | Article Drosophila Rae1 controls the abundance of the ubiquitin ligase Highwire in post-mitotic neurons * Xiaolin Tian1 * Jing Li1 * Vera Valakh2 * Aaron DiAntonio2 * Chunlai Wu1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1267–1275Year published:(2011)DOI:doi:10.1038/nn.2922Received12 May 2011Accepted25 July 2011Published online28 August 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 evolutionarily conserved Highwire (Hiw)/Drosophila Fsn E3 ubiquitin ligase complex is required for normal synaptic morphology during development and axonal regeneration after injury. However, little is known about the molecular mechanisms that regulate the Hiw E3 ligase complex. Using tandem affinity purification techniques, we identified Drosophila Rae1 as a previously unknown component of the Hiw/Fsn complex. Loss of Rae1 function in neurons results in morphological defects at the neuromuscular junction that are similar to those seen in hiw mutants. We found that Rae1 physically and genetically interacts with Hiw and restrains synaptic terminal growth by regulating the MAP kinase kinase kinase Wallenda. Moreover, we found that the Rae1 is both necessary and sufficient to promote Hiw protein abundance, and it does so by binding to Hiw and protecting Hiw from autophagy-mediated degradation. These results describe a previously unknown mechanism that selectively controls H! iw protein abundance during synaptic development. View full text Figures at a glance * Figure 1: Rae1 and Hiw interact with each other in neurons. () The Hiw-associated complex was purified by TAP (see Online Methods) and analyzed by one-dimensional SDS-PAGE gel followed by Sypro Ruby staining. Mass spectrometry identified the full-length Hiw, β-tubulin, Rae1 and Fsn, indicated by arrows. () The Rae1-associated complex purified from fly brains by TAP was analyzed by one-dimensional SDS-PAGE gel followed by Coomassie blue G-250 staining. Mass spectrometry identified Hiw, Nup98 and Rae1, indicated by arrows. () Larval brain lysate of wild-type (WT), hiw null mutant (hiwΔN) and Fsn mutant (Fsnf06595/Df(2R)7872) flies was subject to co-immunoprecipitation with antibody to Hiw (Hiw2b). Both the input and the immunoprecipitated complexes were analyzed by western blots with antibodies to Hiw or Rae1. The membrane was also blotted with antibody to β-tubulin to ensure equal amount of total proteins in the input samples. Full-length blots are presented in Supplementary Figure 8. * Figure 2: Generation of Rae1 mutant alleles. () The gene structure of Rae1. The black-arrowed boxes indicate exons. A transposable element, P{GawB}NP3499, located ~400 bp upstream of the first exon of Rae1 was used to generate imprecise excision mutants of Rae1. PCR analysis indicates that ~900 bp and a ~1.5-k bp DNA fragment in the Rae1 promoter region are missing in Rae1EX28 and Rae1EXB12, respectively. () Western blot analysis on Rae1 protein in the brain lysate of wild-type, Df(2R)5764/+, Rae1EX28/+, or late second instar Rae1EX28, Rae1EX28/Df(2R)5764(/Df) and Rae1EXB12 /Df(2R)5764 larvae. β-tubulin was used as the loading control. Full-length blots are presented in Supplementary Figure 9. () Quantification of Rae1 protein levels in the Rae1 heterozygous and homozygous mutant brains. Rae1 protein levels were first normalized to β-tubulin protein levels and are presented as percentage of wild-type level. *P < 0.05, **P < 0.001. Error bars denote s.e.m. * Figure 3: Rae1 is required to restrain synaptic terminal growth at the NMJ. () Representative confocal images of segment A3 muscle 6/7 synapses stained for both DVGLUT (green) and FasII (red), in late 2nd/early 3rd instar wild-type, Rae1EX28, Rae1EX28 presynaptic rescue (Rae1EX28; elav-Gal4/UAS–NTAP-Rae1) and Rae1EX28wallenda suppression (Rae1EX28; wnd3) larvae. Scale bar represents 10 μm. (,) Quantification of bouton number () and size () in wild-type, Rae1EX28/Df, Rae1EX28, Rae1EX28 rescue, Rae1EX28; wnd3 suppression and hiwΔN larval NMJs (segment A3 muscle 6/7; n = 20, 21, 18, 20, 20 and 11, respectively, for bouton number; n = 271, 465, 548, 726, 486 and 826, respectively, for bouton size). There was no significant difference in either bouton number or bouton size between Rae1EX28 and Rae1EX28/Df (P > 0.1 for both comparisons). *P < 0.001. Error bars denote s.e.m. * Figure 4: Rae1 genetically interacts with hiw to restrain synaptic terminal growth. () Representative confocal images of muscle 4 synapses stained for both DVGLUT (green) and FasII (red) in wild-type, Rae1EX28/+, hiwND51, hiwND51; Rae1EX28/+, hiwΔN and hiwΔN; Rae1EX28/+ larvae. Scale bar represents 10 μm. () Quantification of bouton number at muscle 4 NMJs in wild-type, Rae1EX28/+, hiwND51, hiwND51; Rae1EX28/+, hiwΔN and hiwΔN; Rae1EX28/+ larvae (n = 27, 23, 42, 46, 23 and 25 cells, respectively). There was a significant increase in the number of boutons formed in hiwND51; Rae1EX28/+ compared with hiwND51 3rd instar larval NMJs (*P < 0.001). The number of boutons in the hiwΔN; Rae1EX28/+ double mutant was not significantly different from that in hiwΔN (**P > 0.5), demonstrating a lack of enhancement of the hiwΔN phenotype by Rae1EX28/+. Error bars denote s.e.m. * Figure 5: A structure and function analysis of Hiw functional domains. () Schematic presentation of NTAP-tagged (NT-) and HM-tagged (HM-) hiw transgenes encoding full-length, mutated or truncated Hiw proteins. Hiw functional domains are marked with colored boxes. The positions of amino-acid substitution in NT-HiwΔRING and the added amino acid residue necessary to maintain the reading frame in NT-Hiw-RCC1 and NT-Hiw-PHR are indicated. The right column summarizes the ability of the given hiw transgene to rescue the synaptic overgrowth phenotype in hiw mutants, to cause dominant negative overgrowth phenotype when expressed in wild-type background and to bind to Rae1. () Western blot analysis of fly brain lysate using antibody to TAP (PAP, Sigma) or Myc revealed the expression of wild-type and mutant hiw transgenes in predicted size. () Representative confocal images of muscle 4 synapses stained for DVGLUT (green) and FasII (red) in wild-type (+/hiwND8; +/+; GS-elav-Gal4/+), NT-Hiw overexpression (+/hiwND8; UAS-NT-hiw/+; GS-elav-Gal4/+), NT-Hiw-NT! overexpression (+/hiwND8; UAS-NT-hiw-NT/+; GS-elav-Gal4/+), NT-Hiw-CT1000 overexpression (+/hiwND8; +/+; GS-elav-Gal4/UAS-NT-hiw-CT1000), NT-Hiw-PHR overexpression (+/hiwND8; +/+; GS-elav-Gal4/UAS-NT-hiw-PHR), HM-Hiw-HindIII overexpression (+/hiwND8; UAS-HW-hiw-HindIII/+; GS-elav-Gal4/+), NT-Hiw-RCC1 overexpression (NT-hiw-RCC1/hiwND8; +/+; GS-elav-Gal4/+), or NT-Hiw-CT overexpression (+/hiwND8; +/+; GS-elav-Gal4/UAS-NT-hiw-CT) 3rd larval NMJs. Overexpression is indicated by an up arrow. Scale bar represents 10 μm. () Quantification of the number of boutons at muscle 4 NMJs in wild-type larvae or larvae overexpressing wild-type or mutant hiw transgenes (n = 23, 24, 26, 27, 26, 21, 25 and 27, respectively; *P < 0.001). Error bars denote s.e.m. * Figure 6: Rae1 interacts with a fragment in the Hiw C-terminal region, and coexpression of Rae1 with NT-Hiw-CT suppresses the NT-Hiw-CT–induced dominant-negative overgrowth phenotype. () Indicated NTAP-tagged hiw transgenes (described in Fig. 5) and a TAP only transgene were expressed in neurons under the control of the BG380-Gal4 driver. Larval brain lysates from each sample were subject to IgG pulldown. Both the inputs and the IgG pulldown complexes were analyzed by western blot using antibody to Rae1. Rae1 was present in all of the pulldown complexes except for those from TAP only, NT-Hiw-CT1000 and NT-Hiw-NT. Full-length blots are presented in Supplementary Figure 10. () Quantification of the interaction between various Hiw transgenic proteins and Rae1. Rae1 intensities in TAP pulldown blots were normalized to both the intensities and molecular weights of the corresponding TAP pulldown proteins. () Representative confocal images of muscle 4 synapses stained for both DVGLUT (green) and FasII (red) in wild-type (BG380/Y;+/+; +/+), NT-Hiw-CT and GAP-GFP (BG380/Y; UAS-GAP-GFP/+; UAS-NT-hiw-CT/+), and NT-Hiw-CT and Rae1 (BG380/Y; +/+; UAS-NT-hiw-CT/UAS-GFP! -Rae1) larvae. Scale bar represents 10 μm. () Quantification of number of boutons at muscle 4 NMJs in wild-type, NT-Hiw-CT overexpression (BG380/Y; +/+; UAS-NT-hiw-CT/+), NT-Hiw-CT and GAP-GFP, and NT-Hiw-CT and Rae1 (n = 24, 27, 32 and 26, respectively) larvae. Error bars denote s.e.m.; *P < 0.001. * Figure 7: Rae1 promotes Hiw abundance. () Neuronal expression of neither Hiw nor Rae1 rescues the synaptic terminal overgrowth phenotype caused by the loss of function of the other gene. Quantification of the number of boutons in wild-type, Rae1EX28, Rae1EX28; Hiw rescue (Res) (Rae1EX28; elav-Gal4/UAS-GFP-hiw), hiwΔN and hiwΔN; Rae1 rescue (hiwΔN; elav-Gal4/UAS-NTAP-Rae1) larval NMJs (segment A3 muscle 6/7; n = 12, 12, 12, 7 and 11, respectively). () Representative confocal images of wild-type (BG380-Gal4/+; +/+; UAS-GFP-hiw/+) or Rae1 mutant (BG380-Gal4/+; Rae1EX28; UAS-GFP-hiw/+) ventral ganglions with UAS-GFP-hiw trangene expressed in neurons. The larvae were stained with antibodies to horseradish peroxidase (HRP, red) and GFP (green). Scale bar represents 10 μm. () Western blots of total proteins extracted from wild-type, hiwΔN, Rae1EX28 and Rae1EX28 rescue (Rae1EX28; elav-Gal4/UAS-NTAP-Rae1) larval brains probed with indicated antibodies. Full-length blots are presented in Supplementary Figure 11. () We! stern blot analysis on total proteins extracted from hiwΔN, wild-type (C155-Gal4/+) and Rae1 expression (C155-Gal4/+; UAS-NTAP-Rae1/+) larval brains. Full-length blots are presented in Supplementary Figure 12. () Quantification of Hiw protein levels in the wild-type and the Rae1 expression larval brains (*P < 0.001, based on seven independent experiments). Arrows indicate a 38-kDa endogenous Rae1 protein and the arrowheads indicate the 58-kDa transgenic NTAP-Rae1 protein. Error bars denote s.e.m. * Figure 8: Rae1-Hiw interaction prevents autophagy-mediated degradation of Hiw protein. () Representative confocal images of segment A3 muscle 6/7 synapses stained for both DVGLUT (green) and FasII (red), in wild-type, Rae1EX28, Rae1EX28; atg1/+, Rae1EX28; atg2/+ and Rae1EX28; atg18/+ wandering larvae. Scale bar represents 10 μm. () Quantification of the number of boutons in wild-type, atg1/+, atg2/+, atg18/+, Rae1EX28, Rae1EX28; atg1/+, Rae1EX28; atg2/+ and Rae1EX28; atg18/+ larval NMJs (segment A3 muscle 6/7; n = 12, 12, 12, 12, 12, 16, 12 and 21, respectively). () Western blot analysis on total proteins extracted from wild-type, Rae1EX28, Rae1EX28; atg1/+, Rae1EX28; atg2/+ and Rae1EX28; atg18/+ larval brains. The neural form of β-catenin (82 kDa, see Online Methods) was used as an internal control of neuronal proteins. Full-length blots are presented in Supplementary Figure 13. () Quantification of Hiw protein levels in wild-type, Rae1EX28, Rae1EX28; atg1/+, Rae1EX28; atg2/+ and Rae1EX28; atg18/+ larval brains (based on six independent experiments). () Rep! resentative confocal images of muscle 4 synapses stained for both DVGLUT (green) and FasII (red) in wandering larvae of wild-type (C155-Gal4/+), Atg1 Gap-GFP (C155-Gal4/+; UAS-Gap-GFP/+; UAS-Atg16B/+), and Atg1 NTAP-Rae1 (C155-Gal4/+; UAS-NTAP-Rae1/+; UAS-Atg1/+). Scale bar represents 10 μm. () Quantification of the number of boutons at muscle 4 synapses in wild-type, Rae1 expression (C155-Gal4/+; UAS-NTAP-Rae1/+), Atg1 Gap-GFP and Atg1 NTAP-Rae1 wandering larvae (segment A2–4, n = 28, 27, 33 and 34, respectively). Error bars denote s.e.m. *P < 0.001, **P < 0.05. Author information * Abstract * Author information * Supplementary information Affiliations * Neuroscience Center of Excellence, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA. * Xiaolin Tian, * Jing Li & * Chunlai Wu * Department of Developmental Biology, Washington University School of Medicine, St. Louis, Missouri, USA. * Vera Valakh & * Aaron DiAntonio Contributions C.W. and A.D. initiated the project and conducted the Hiw structure and function analysis. C.W. directed the rest of the studies. X.T. and C.W. conceived and designed the experiments. X.T., J.L. and C.W. performed the experiments. V.V. contributed to the characterization of the Rae1 mutant phenotype. C.W. and X.T. wrote the manuscript with input from A.D. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Chunlai Wu Author Details * Xiaolin Tian Search for this author in: * NPG journals * PubMed * Google Scholar * Jing Li Search for this author in: * NPG journals * PubMed * Google Scholar * Vera Valakh Search for this author in: * NPG journals * PubMed * Google Scholar * Aaron DiAntonio Search for this author in: * NPG journals * PubMed * Google Scholar * Chunlai Wu Contact Chunlai Wu Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Figures 1–13, and Supplementary Tables 1 and 2 Additional data - Local Ca2+ detection and modulation of synaptic release by astrocytes
- Nat Neurosci 14(10):1276-1284 (2011)
Nature Neuroscience | Article Local Ca2+ detection and modulation of synaptic release by astrocytes * Maria Amalia Di Castro1, 2 * Julien Chuquet1, 2 * Nicolas Liaudet1 * Khaleel Bhaukaurally1 * Mirko Santello1 * David Bouvier1 * Pascale Tiret1 * Andrea Volterra1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1276–1284Year published:(2011)DOI:doi:10.1038/nn.2929Received09 June 2011Accepted11 August 2011Published online11 September 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 Astrocytes communicate with synapses by means of intracellular calcium ([Ca2+]i) elevations, but local calcium dynamics in astrocytic processes have never been thoroughly investigated. By taking advantage of high-resolution two-photon microscopy, we identify the characteristics of local astrocyte calcium activity in the adult mouse hippocampus. Astrocytic processes showed intense activity, triggered by physiological transmission at neighboring synapses. They encoded synchronous synaptic events generated by sparse action potentials into robust regional (~12 μm) [Ca2+]i elevations. Unexpectedly, they also sensed spontaneous synaptic events, producing highly confined (~4 μm), fast (millisecond-scale) miniature Ca2+ responses. This Ca2+ activity in astrocytic processes is generated through GTP- and inositol-1,4,5-trisphosphate–dependent signaling and is relevant for basal synaptic function. Thus, buffering astrocyte [Ca2+]i or blocking a receptor mediating local astrocyte Ca! 2+ signals decreased synaptic transmission reliability in minimal stimulation experiments. These data provide direct evidence that astrocytes are integrated in local synaptic functioning in adult brain. View full text Figures at a glance * Figure 1: Imaging Ca2+ activity in micrometric astrocytic process domains in the adult hippocampus. () Tridimensional morphology of an astrocyte arborescence built from two-photon z-stack imaging of TxR dye signal. The patch pipette appears on top as a large cylinder. In red, a process fully lying in the focal plane selected for Ca2+ imaging. For analysis of local Ca2+ dynamics, the process was divided in subregions of ~1 μm2, each represented in a different color. All subregions were within the tridimensional structure of the process, but some showed branches exiting the focal plane (green sample), whereas others were not branched (blue sample). () Immunohistochemistry image of the neuropil of the dentate molecular layer showing processes of an astrocyte (cytosolic EGFP, red) surrounded by synaptic boutons (synaptophysin-positive puncta, white) all along the structure. Scale bar, 5 μm. () Time-lapse Ca2+ imaging in an astrocytic process (the same one selected in ). Different types of Ca2+ events were observed, from localized (arrow in frame 2) to widespread (frame 4). T! ime between frames, 8 s; scale bar, 5 μm. * Figure 2: Two types of Ca2+ events, focal and expanded, in astrocytic processes. () Example of Ca2+ activity in an astrocytic process imaged in frame scan mode (3 Hz) for 80 s. Left: process reconstruction and subdivision in 37 subregions of ~1 μm2. Middle: ΔG/R traces showing occurrence of Ca2+ peaks in contiguous process subregions. Red highlights identify expanded Ca2+ events, relatively high-amplitude transients with recurrent pattern in several subregions. Blue highlights identify focal Ca2+ events, small transients often limited to one subregion and randomly occurring in most subregions. Right: magnified traces of boxed examples of focal and expanded Ca2+ events. () Representative example of a focal Ca2+ event imaged at high temporal resolution (line-scan mode, 618 Hz). A 15-μm line was drawn over an astrocytic process lying in the focal plane. The resulting image of [Ca2+]i changes, analyzed in 1.5-μm subregions (color-coded), shows that Ca2+ rise diffuses from a central subregion to 1 or 2 lateral subregions. () Recurrent spatial profile of f! ocal Ca2+ events (average plotted in blue). Shown is a subset consisting of 14 events. () Temporal characteristics of focal Ca2+ events: top, rise time distribution; bottom, duration distribution (at FWHM). () Stereotypical shape of focal events (average plot of 14 events). () Representative example of an expanded Ca2+ event imaged in line-scan mode as in . There are more subregions involved and a greater magnitude in this type of event than in focal events. * Figure 3: Ca2+ events in astrocytic processes are triggered by spontaneous and action potential–dependent synaptic activity. () Representative patch-clamp recordings of sPSCs in dentate granule cells. Top: sPSC activity before (control) and upon TTX application. Bottom: sPSC activity in control slices (left) and in Baf-incubated slices (right). (,) Histograms showing sPSC frequency () and amplitude () in control condition and with TTX or Baf (control data include both sets of experiments). Results presented as means ± s.e.m. TTX and Baf significantly and differently decreased sPSC frequency and amplitude (TTX, paired t-test; Baf, unpaired t-test). (–) Top: representative ΔG/R traces of Ca2+ activity in a subregion (arrow) of an astrocytic process and raster plots for all the subregions in control conditions (), TTX () and Baf (). Red, expanded Ca2+ events; blue, focal Ca2+ events. Bottom: histograms of mean frequencies per subregion for focal Ca2+ events (blue bars), and mean frequencies per process and average maximal surface extension for expanded Ca2+ events (red bars) in control conditions! (), TTX (), Baf (). Experiments and statistics as in –. *P < 0.05; **P < 0.01; ***P < 0.001. * Figure 4: Local synaptic release evoked by sucrose triggers focal Ca2+ events in astrocytic processes. () Experimental arrangement showing pipette position for local puff applications relative to astrocytic process where Ca2+ dynamics are monitored. Process segmentation in colored subregions superimposed on TxR image. Scale bar, 2 μm. () Histograms comparing focal Ca2+ events frequency 1 s before (pre) and 1 s after (post) a 5-ms puff of ACSF (control), hypertonic solution (sucrose) or sucrose in Baf-incubated slices. () Histograms showing temporal occurrence of focal Ca2+ events before and after sucrose puff (total number of events across all trials counted in 250-ms bins for 8 s). () Stereotypical shape of focal Ca2+ events occurring ≤ 500 ms after the stimulus (sucrose) compared to events occurring before the stimulus (control). Results are presented as means ± s.e.m. Paired Student t-test for single comparisons, unpaired for experiments with Baf; ANOVA with Bonferroni post hoc analysis for multiple comparisons; *P < 0.05. * Figure 5: Ca2+ events in astrocytic processes are mediated by GTP- and InsP3-dependent signaling. () Histograms: mean frequencies of focal Ca2+ events (per subregion; top) and expanded Ca2+ events (per process; bottom) in control conditions, with no external Ca2+ (Ca2+-free), with GDP-β-S in the astrocyte (AstroGDP-β-S), with heparin in the astrocyte (Astroheparin) and in Itpr2−/− mice. Control data are from all sets of experiments. Results are presented as means ± s.e.m. For Ca2+-free: paired t-test; for AstroGDP-β-S, Astroheparin and Itpr2−/−: unpaired t-test. *P < 0.05; **P < 0.01; ***P < 0.001. () Time-lapse Ca2+ imaging in the processes of an astrocyte infused with heparin. Representative pseudocolor Ca2+ activity images (superimposed on corresponding morphology images) taken at the beginning of the experiment, when both focal and expanded (exp) Ca2+ events occur in the process (control, top panels); ~10 min after patch-clamping the cell in whole-cell mode with heparin-containing solution (bottom left, Astheparin), when all Ca2+ activity is abolished; an! d 10 min after starting thapsigargin perfusion (bottom right, Astheparin + thapsi), when strong local [Ca2+]i elevations are seen despite the presence of heparin. Scale bar, 2 μm. In all frames, dye-loaded patch pipette is seen on the left. () Representative ΔG/R traces comparing Ca2+ activity in contiguous subregions of an astrocytic process from control (left) and Itpr2−/− mice (right) imaged in frame scan mode (3 Hz) for 150 s. Red highlight, expanded Ca2+ events; blue highlight, focal Ca2+ events. Local Ca2+ activity is nearly absent in Itpr2−/− mice. * Figure 6: Blockade of Ca2+ activity in astrocytic processes decreases basal transmission at local synapses: role of P2Y1R. () BAPTA infusion in an astrocyte decreases release probability at neighboring excitatory synapses. Left: experimental arrangement for minimal stimulation experiments. An astrocyte, present within the dendritic arbor of the recorded granule cell and ~20–30 μm away from the stimulating pipette (Pipstim), is patch-clamped (Pipastro) with (or without) BAPTA-containing solution. Right: representative traces and time course of synaptic current changes in a granule cell; −5 to 0 min, Pipastro with BAPTA in cell-attached mode; 0 to 10 min, Pipastro in whole-cell mode. () Failure rate changes in individual granule cells from before to 10 min after entering whole-cell mode with BAPTA in astrocyte (AstroBAPTA). () Histograms showing synaptic failures and synaptic potency (SynPot) with and without BAPTA. Black bars, AstroBAPTA; gray bars, AstroCTRL (Pipastro in whole-cell mode without BAPTA). () Selective expression of P2Y1R (green) in astrocytic processes (red) in HDML. Scale bar! , 2 μm. () Effect of the P2Y1R antagonist MRS 2179 on basal frequency of focal (blue bars) and expanded (red bars) Ca2+ events in astrocytic processes. (Analysis of subregion specificity in Supplementary Fig. 13.) () Representative traces and time course of synaptic current changes in a granule cell before, during and after MRS 2179 application. () Failure rate changes in individual granule cells during MRS 2179 application, reversible upon drug washout. Dashed lines, cells in which MRS 2179 did not increase failures. () Histograms showing synaptic failure rates upon application of MRS 2179, in basal conditions (black bars) and with BAPTA in the astrocyte (gray bars). Results in ,,, means ± s.e.m., paired t-test; *P < 0.05. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Maria Amalia Di Castro & * Julien Chuquet Affiliations * Department of Cell Biology and Morphology, University of Lausanne, Lausanne, Switzerland. * Maria Amalia Di Castro, * Julien Chuquet, * Nicolas Liaudet, * Khaleel Bhaukaurally, * Mirko Santello, * David Bouvier, * Pascale Tiret & * Andrea Volterra Contributions M.A.D.C. performed imaging, electrophysiology (including minimal stimulations) and data analysis; J.C. coordinated imaging, software development and performed imaging and data analysis; and the three next authors contributed equally: N.L. developed software for imaging data analysis and participated in imaging and imaging data analysis; K.B. performed imaging and electrophysiology and contributed to software development in the first part of the project; and M.S. performed imaging, electrophysiology and electrophysiology analysis. D.B. performed immunohistochemistry, P.T. performed imaging and data analysis in the last part of the project, and A.V. coordinated the project and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Andrea Volterra Author Details * Maria Amalia Di Castro Search for this author in: * NPG journals * PubMed * Google Scholar * Julien Chuquet Search for this author in: * NPG journals * PubMed * Google Scholar * Nicolas Liaudet Search for this author in: * NPG journals * PubMed * Google Scholar * Khaleel Bhaukaurally Search for this author in: * NPG journals * PubMed * Google Scholar * Mirko Santello Search for this author in: * NPG journals * PubMed * Google Scholar * David Bouvier Search for this author in: * NPG journals * PubMed * Google Scholar * Pascale Tiret Search for this author in: * NPG journals * PubMed * Google Scholar * Andrea Volterra Contact Andrea Volterra Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Video 1 (3M) Ca2+ activity in astrocytic processes in basal condition. Ca2+ imaging sequence (150 s, 3 Hz, accelerated 5 times) in the processes of an astrocyte in the adult dentate molecular layer. Ca2+ activity comprises "focal" Ca2+ events, randomly recurrent, fast local transients; and "expanded" Ca2+ events, less frequent, large scale, longer-lasting transients. * Supplementary Video 2 (2M) From astrocytes to micrometric sub-regions: the segmentation principle. The movie summarizes the different steps of segmentation of astrocytic processes into sub-regions of ~1 μm+ in which Ca2+ activity is studied. 3D morphology is built from the fluorescence signal of the Ca2+-insensitive dye Texas Red. * Supplementary Video 3 (2M) Effect of TTX on Ca2+ activity in astrocytic processes. Dual sequence (150 s, 3 Hz) of Ca2+ activity in basal condition (left) and upon TTX treatment (right). Blocking action potentials affects primarily expanded Ca2+ events, whereas focal Ca2+ activity is largely maintained. PDF files * Supplementary Text and Figures (741K) Supplementary Figures 1–13, Supplementary Data 1 and 2 Additional data - Presynaptic regulation of quantal size: K+/H+ exchange stimulates vesicular glutamate transport
- Nat Neurosci 14(10):1285-1292 (2011)
Nature Neuroscience | Article Presynaptic regulation of quantal size: K+/H+ exchange stimulates vesicular glutamate transport * Germaine Y Goh1 * Hai Huang2, 4 * Julie Ullman1, 3, 4 * Lars Borre1 * Thomas S Hnasko1 * Laurence O Trussell2 * Robert H Edwards1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1285–1292Year published:(2011)DOI:doi:10.1038/nn.2898Received23 May 2011Accepted11 July 2011Published online28 August 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 amount of neurotransmitter stored in a single synaptic vesicle can determine the size of the postsynaptic response, but the factors that regulate vesicle filling are poorly understood. A proton electrochemical gradient (ΔμH+) generated by the vacuolar H+-ATPase drives the accumulation of classical transmitters into synaptic vesicles. The chemical component of ΔμH+ (ΔpH) has received particular attention for its role in the vesicular transport of cationic transmitters as well as in protein sorting and degradation. Thus, considerable work has addressed the factors that promote ΔpH. However, synaptic vesicle uptake of the principal excitatory transmitter glutamate depends on the electrical component of ΔμH+ (Δψ). We found that rat brain synaptic vesicles express monovalent cation/H+ exchange activity that converts ΔpH into Δψ, and that this promotes synaptic vesicle filling with glutamate. Manipulating presynaptic K+ at a glutamatergic synapse influenced quanta! l size, indicating that synaptic vesicle K+/H+ exchange regulates glutamate release and synaptic transmission. View full text Figures at a glance * Figure 1: Synaptic vesicles express a Na+/H+ exchange activity. (,) Measurement of ΔpH across synaptic vesicles using acridine orange fluorescence. The quenching of fluorescence reflects the generation of an inside-acidic ΔpH. Synaptic vesicles preloaded with Na+ (LP2/Na+) and pretreated with bafilomycin to inhibit the V-ATPase were added (time point indicated by arrowhead) to either 150 mM sodium gluconate (Naout) or 150 mM choline gluconate (choout) (), or to 150 mM choline gluconate buffer with (black) or without (gray) CCCP (). CCCP did not affect the rate or extent of acidification, indicating that the outwardly directed Na+ gradient does not drive H+ flux through changes in membrane potential. () The acidification of synaptic vesicles by an outwardly directed Na+ gradient was inhibited by EIPA (15 μM). () The monovalent cations Na+, Li+ and K+ (20 mM) were added to synaptic vesicles that were acidified by an outwardly directed Na+ gradient. Data are presented as in . (,) After acidification by an outwardly directed Na+ gradient,! synaptic vesicles alkalinized in a dose-dependent manner to Na+ () and K+ (). The mean initial rates of alkalinization varied as a function of cation concentration (bottom, n = 3), with Vmax, Km and the Hill coefficient (nH) obtained by fitting to the Hill equation. All horizontal scale bars indicate 30 s and all vertical scale bars indicate 10 afu. Error bars represent s.e.m. * Figure 2: Synaptic vesicles lack a detectable K+ channel conductance. (,) Measurement of ΔpH across synaptic vesicles using acridine orange fluorescence. Synaptic vesicles were acidified by preloading with 200 mM NH4+ (LP2/NH4+) and dilution into NH4+-free buffer containing no K+ (cho+ outside, ) or 150 mM K+ (K+ outside, ). In each panel, arrows in the same direction indicate addition to one trace, and arrows in the opposite direction indicate addition to the other trace. CCCP (5 μM) had no effect in the absence of K+, with or without the addition of 50 nM valinomycin (val). CCCP alone also had no effect in the presence of K+, indicating the absence of a substantial K+ conductance. However, the prior addition of valinomycin enabled CCCP to alkalinize the vesicles rapidly, indicating that CCCP would have produced alkalinization in the presence of an endogenous K+ conductance. Addition of 100 mM NH4+ to the external medium immediately reversed the acidification. * Figure 3: Cation/H+ exchange activity is present on glutamatergic synaptic vesicles and increases Δψ. () The quenching of acridine orange fluorescence revealed progressive acidification of 100 μg LP2 protein by 1 mM MgATP, 2 mM choline chloride and 10 mM choline glutamate (left). Gluconate salts of NMDG+ (gray), Na+ (blue) or K+ (red) (75 mM final concentration) were added at the arrow and compared with vesicles that received no additional cation (black). The vertical scale bar indicates 30 afu and the horizontal scale bar represents 60 s. The mean fluorescence change 150 s after the arrow shows that Na+ and K+, but not NMDG+, reversed acidification in a dose-dependent manner (right). *P < 0.05, ***P < 0.001 by two-way ANOVA with post hoc Bonferroni test (n = 6–12 samples). Glu, glutamate. () Synaptic vesicle membrane potential was measured using the ratiometric fluorescence of oxonol VI, with an increase in the ratio reflecting greater inside-positive potential. Arrowheads indicate sequential addition of 1 mM MgATP, 50 mM potassium gluconate, 10 mM (NH4)2 tartrate and 5 ! μM CCCP (left), with the order of K+ and NH4+ addition reversed in the middle panel, and the two traces superimposed on the right. Bar graphs show the mean change in fluorescence ratio, with the order of addition indicated by numbers on or above the bars (n = 9 and 4 for left and middle panels, respectively; P < 0.05 for the effect of NH4+ after versus before K+, and P < 0.0001 for the effect of K+ amplitude before and after NH4+, by two-tailed unpaired t test). The vertical scale bar indicates a ratio of 0.10 and the horizontal scale bar represents 60 s. Data are presented as mean ± s.e.m. * Figure 4: Monovalent cations increase glutamate transport into synaptic vesicles. () The uptake of 3H-glutamate (filled bars) or 3H-aspartate (open bars) by rat brain synaptic vesicles was measured at 10 min in assay buffer containing either 2 mM choline chloride and 148 mM choline gluconate (cho) or 2 mM KCl and 148 mM potassium gluconate (K), in either 50 μM or 1 mM amino acid, with or without the VGLUT inhibitor Evans Blue (EB) (at 10 and 100 μM for left and right panels, respectively). Uptake was normalized to that observed with choline in the absence of Evans Blue. *P < 0.05, **P < 0.01 (n = 3). (,) Time course () and kinetics () of glutamate uptake in either 150 mM choline (open circles) or 150 mM K+ (filled circles). The time course was performed using 1 mM glutamate and the kinetic analysis was carried out at 1 min, yielding a mean Km of 1.02 ± 0.08 mM for K+ and 1.27 ± 0.04 mM for cho (P < 0.05), and a mean Vmax of 1.26 ± 0.33 nmol mg−1 and 1.12 ± 0.24 nmol mg−1 for K+ and cho, respectively (P = 0.31) (n = 3). () Dose response of glutam! ate uptake to potassium, with total choline and potassium gluconate adjusted to 150 mM, and uptake normalized to that observed in the absence of potassium. () Uptake of 1 mM 3H-glutamate for 10 min in the presence of 150 mM potassium, sodium or lithium gluconate, normalized to that in 150 mM choline (n = 3). Statistical analysis was by two-tailed, paired t test, and values indicate mean ± s.e.m. * Figure 5: Cation/H+ exchange stimulates glutamate uptake by increasing Δψ. () Synaptic vesicle uptake of 1 mM 3H-glutamate was measured in either 150 mM choline or 150 mM K+, in the presence or absence of 10 mM (NH4)2 tartrate (top). The fold-stimulation of transport by K+ in the presence or absence of NH4+ and by NH4+ in the presence or absence of K+ indicates that NH4+ partially occluded the effect of K+ and vice versa (P < 0.05 by two-tailed paired t tests, n = 4). () The uptake of 3H-glutamate (1 mM) by synaptic vesicles was measured in 15 mM potassium gluconate/135 mM choline gluconate with or without either 50 nM nigericin (Nig) or 50 nM valinomycin. **P < 0.01, NS indicates not significant (P = 0.76), two-tailed paired t tests (n = 3). Values indicate mean ± s.e.m. * Figure 6: Presynaptic K+ regulates mEPSC amplitude at the calyx of Held. (–) Paired recordings were performed from both pre- and postsynaptic elements at the calyx of Held. mEPSCs were recorded postsynaptically immediately (within 2 min) and 25–30 min after break-in to the presynaptic terminal with a pipette containing either 130 mM K+ solution (,), 130 mM NMDG+ (zero K+) solution (,) or high K+ solution with 50–100 μM EIPA (,). All presynaptic solutions contained 10 mM Na+ to mimic physiological conditions. Recordings made <2 min (left) and 25–30 min (right) after break-in to the presynaptic terminal are shown in –. Vertical and horizontal scale bars indicate 20 pA and 2 s, respectively. Cumulative probability histograms of mEPSC amplitude early (black) and late (red) during dialysis of the nerve terminal showed relatively little difference when the pipettes were filled with high K+ solution (P > 0.05, n = 4, ), but shifted toward smaller amplitudes with a presynaptic solution low in K+ (P < 0.001, n = 5, ) or with solution containing! high K+ and EIPA (P < 0.05, n = 4, ). () Time course of the average data from paired recordings with presynaptic pipettes containing high K+ (black, n = 4), low K+ (red, n = 5) or high K+ solution with 50–100 μM EIPA (blue, n = 4). Each point represents the mean and s.e.m. of recording over the previous minute. All presynaptic solutions contained 10 mM Na+ except for the low Na+ solution (open, n = 5), which contained only 0.6 mM Na+. *P < 0.05, **P < 0.01. Error bars represent s.e.m. * Figure 7: ΔpH-driven 22Na+ uptake into glutamatergic synaptic vesicles is sensitive to EIPA. () Synaptic vesicle uptake of 22Na+ was measured for 10 min in ATP and either 10 mM choline glutamate or 10 mM choline aspartate, and the results were normalized to uptake in glutamate (n = 25). () 22Na+ uptake was measured in the presence of ATP, glutamate and either Evans Blue (EB, 100 μM), bafilomycin Al (baf, 0.5 μM), ammonium tartrate (NH4+, 10 mM), EIPA (15 μM) or the Na+ ionophore monensin (mon, 5 μM) as positive control (n = 3–5). () Uptake in 10 mM choline glutamate or aspartate, with or without 2-APB (50 μM), ruthenium red (RuR, 100 μM), tetraethylammonium (TEA, 5 mM), TTX (0.5 μM) and EIPA (50 μM) (n = 3–5). () EIPA inhibited 22Na+ uptake more potently in vesicles acidified with glutamate (n = 3–6). () Amiloride also inhibited 22Na+ uptake, but less potently than EIPA (n = 5–6). *P < 0.05, **P < 0.01 and ***P < 0.0001; NS indicates P = 0.99 by two-tailed paired t tests. () The uptake of 3H-glutamate was measured for 10 min in assay buffer containin! g 4 mM MgATP, 2 mM choline chloride, 10 mM glutamate, 10 mM NMDG gluconate, and either 150 mM NMDG gluconate (black), 150 mM sodium gluconate (blue) or 150 mM potassium gluconate (red), with or without EIPA. After subtraction of the background in 100 μM Evans Blue, uptake was normalized to that observed in NMDG gluconate without EIPA (left). The uptake in Na+ or K+ was then normalized to that in NMDG+ (right). #P < 0.05, ##P < 0.01 by two-way ANOVA (n = 15–18); ns, P > 0.33. Data indicate mean ± s.e.m. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Hai Huang & * Julie Ullman Affiliations * Departments of Physiology and Neurology, Graduate Programs in Neuroscience, Cell Biology and Bioengineering, University of California, San Francisco, San Francisco, California, USA. * Germaine Y Goh, * Julie Ullman, * Lars Borre, * Thomas S Hnasko & * Robert H Edwards * Oregon Hearing Research Center and Vollum Institute, Oregon Health and Science University, Portland, Oregon, USA. * Hai Huang & * Laurence O Trussell * Graduate Program in Molecular and Cellular Biology, University of California, Berkeley, Berkeley, California, USA. * Julie Ullman Contributions The biochemical experiments were conducted by G.Y.G., J.U. and T.S.H. in the laboratory of R.H.E. L.B. developed the measurement of 22Na+ uptake. The electrophysiology experiments were performed by H.H. in the laboratory of L.O.T. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Robert H Edwards Author Details * Germaine Y Goh Search for this author in: * NPG journals * PubMed * Google Scholar * Hai Huang Search for this author in: * NPG journals * PubMed * Google Scholar * Julie Ullman Search for this author in: * NPG journals * PubMed * Google Scholar * Lars Borre Search for this author in: * NPG journals * PubMed * Google Scholar * Thomas S Hnasko Search for this author in: * NPG journals * PubMed * Google Scholar * Laurence O Trussell Search for this author in: * NPG journals * PubMed * Google Scholar * Robert H Edwards Contact Robert H Edwards Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (496K) Supplementary Figures 1–4 Additional data - A circadian clock in hippocampus is regulated by interaction between oligophrenin-1 and Rev-erbα
- Nat Neurosci 14(10):1293-1301 (2011)
Nature Neuroscience | Article A circadian clock in hippocampus is regulated by interaction between oligophrenin-1 and Rev-erbα * Pamela Valnegri1, 2 * Malik Khelfaoui3, 4, 5 * Olivier Dorseuil3, 4, 5 * Silvia Bassani1, 2 * Celine Lagneaux3, 4, 5 * Antonella Gianfelice1 * Roberta Benfante1 * Jamel Chelly3, 4, 5 * Pierre Billuart3, 4, 5 * Carlo Sala1, 6 * Maria Passafaro1, 2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1293–1301Year published:(2011)DOI:doi:10.1038/nn.2911Received13 April 2011Accepted21 July 2011Published online28 August 2011Corrected online11 September 2011 Abstract * Abstract * Change history * 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 Oligophrenin-1 regulates dendritic spine morphology in the brain. Mutations in the oligophrenin-1 gene (OPHN1) cause intellectual disability. We discovered a previously unknown partner of oligophrenin-1, Rev-erbα, a nuclear receptor that represses the transcription of circadian oscillators. We found that oligophrenin-1 interacts with Rev-erbα in the mouse brain, causing it to locate to dendrites, reducing its repressor activity and protecting it from degradation. Our results indicate the presence of a circadian oscillator in the hippocampus, involving the clock gene Bmal1 (also known as Arntl), that is modulated by Rev-erbα and requires oligophrenin-1 for normal oscillation. We also found that synaptic activity induced Rev-erbα localization to dendrites and spines, a process that is mediated by AMPA receptor activation and requires oligophrenin-1. Our data reveal new interactions between synaptic activity and circadian oscillators, and delineate a new means of communicat! ion between nucleus and synapse that may provide insight into normal plasticity and the etiology of intellectual disability. View full text Figures at a glance * Figure 1: Oligophrenin-1 interacts with Rev-erbα in yeast two-hybrid system, COS7 cells and in neurons. () Schematic representation of oligophrenin-1 and the C-terminal domain of oligophrenin-1, which was used as bait for the yeast two-hybrid screening, and the results of the yeast two-hybrid screening with schematic representation of the four positive clones of the Rev-erbα protein. Two-hybrid interaction was quantified on the basis of the activation of the three reporter genes (HIS3, LacZ, URA3) (3+, activation of all three reporter genes; 2+, activation of HIS3 and LacZ reporters; 1+, activation of HIS3). Small numbers refer to amino acid (aa) residues. () Mapping of the oligophrenin-1 and Rev-erbα interaction by two-hybrid test. We used two fragments of the C terminus of oligophrenin-1 (550–633 and 634–802 amino acids) as bait and a Rev-erbα fragment (263–614 amino acids) as prey. Interactions were quantified as in . () Distribution of Rev-erbα and oligophrenin-1 in COS7 cells. Myc–Rev-erbα localized to the nucleus. HA–oligophrenin-1 localized to the cytopla! sm (upper panel). Ectopic expression of HA–oligophrenin-1 together with myc–Rev-erbα revealed colocalization of both proteins in the cytoplasm. When Rev-erbα was expressed alone (visualized with DAPI staining, top right) and coexpressed with HA–oligophrenin-1ΔC, it was confined to the nucleus (bottom). Scale bar represents 10 μm. () The interaction between oligophrenin-1 and Rev-erbα was confirmed by co-immunoprecipitation (ip) experiments in COS7 cells. HA–oligophrenin-1 co-immunoprecipitated with myc–Rev-erbα, whereas HA–oligophrenin-1ΔC did not. () GST pulldown experiments. Diagram with the two fragments of the C-terminal tail of oligophrenin-1 used in the GST pulldown assay (top). Purified GST550–633 bound Rev-erbα more strongly than GST676–802 in lysates from rat brain and hippocampal neurons. () Co-immunoprecipitation of oligophrenin-1 and Rev-erbα in rat brain lysate. * Figure 2: Oligophrenin-1 reduces transcriptional repression by Rev-erbα. () HEK cells were transfected with pSV40-RE vector alone, with pSV40-RE and GFP, with pSV40-RE and Rev-erbα, with pSV40-RE, Rev-erbα and oligophrenin-1, and with Rev-erbα and oligophrenin-1ΔC. Rev-erbα protein markedly repressed transcription of the pSV40-RE construct (measured by luciferase activity; RLu, relative light units.). When oligophrenin-1 was also expressed with Rev-erbα, it suppressed Rev-erbα repression and luciferase activity increased. When oligophrenin-1ΔC was expressed with Rev-erbα, luciferase activity was reduced to levels similar to those in cells with no oligophrenin-1 overexpression. Values are means ± s.e.m. The difference between pSV40-RE and Rev-erbα, and pSV40-RE and Rev-erbα and oligophrenin-1 was significant (Student's t test, *P < 0.05). () Western blots show localization of myc–Rev-erbα in the cytoplasm and nucleus in HEK cells transfected with various concentrations of HA–oligophrenin-1. Total protein extract from HEK cells was! separated by SDS-PAGE and probed with antibody to myc, then stripped and re-probed with antibody to HA and tubulin. HA–oligophrenin-1 was only present in cytoplasm. The amount of myc–Rev-erbα present in the cytoplasm increased with the amount of HA–oligophrenin-1 added to the transfection procedure. Data are means ± s.e.m. (at least three experiments) of blot intensities of myc–Rev-erbα as the ratio of density in the cytoplasm to total, normalized to tubulin. **P < 0.01, ***P < 0.001, #P < 0.0001 (ANOVA, Bonferroni post test). () Oligophrenin-1 protected Rev-erbα from degradation. Expression of the dominant-negative form of GSK3β (HA-GSK3βDN) in HEK cells resulted in a near total absence of myc–Rev-erbα (lane 1). Oligophrenin-1 inhibited degradation of myc–Rev-erbα protein (lane 3) in the presence of HA-GSK3βDN. () Myc–Rev-erbα expression normalized to tubulin. Means ± s.e.m. of at least three experiments; #P < 0.0001 (ANOVA, Bonferroni post test).! () Distribution of myc–Rev-erbα in the cytoplasm (C) and n! ucleus (N) of HEK cells overexpressing myc–Rev-erbα alone or with HA–oligophrenin-1, after incubation with or without MG132. Data are presented as in . In the absence of MG132, Rev-erbα was degraded unless oligophrenin-1 was present (left); in the presence of MG132, Rev-erbα was not degraded. Data are presented as means ± s.e.m.; ***P < 0.001 (ANOVA, Bonferroni post test). () Localization of Rev-erbα in the nuclear fraction (N) and post-nuclear supernatant (S) of hippocampus from wild-type (WT) and Ophn1−/− (KO) mice. PSD-95 is a synaptic marker; nuclear protein 4eBP1 was used as a nuclear marker. The total level of Rev-erbα protein was higher in wild-type than in Ophn1−/− hippocampus (1.62 ± 0.06 versus 0.95 ± 0.03, ##P ≤ 0.001, Student t test) and the ratio of Rev-erbα in the supernatant to Rev-erbα in the nucleus was higher in the hippocampus of wild-type than in Ophn1−/− mice. Values are means ± s.e.m. () Western blots of Rev-erbα expressio! n in dendritic and soma fractions of hippocampal neurons overexpressing Ophn1 siRNA or scrambled siRNA, treated or untreated with MG132. In untreated neurons with Ophn1 siRNA, Rev-erbα was largely absent from dendrites and the total quantity was less than in neurons overexpressing scrambled siRNA. When proteasome degradation was blocked by MG132, Rev-erbα levels increased and the protein was present in both dendrites and soma. Histograms show the quantification of total (soma + dendritic) Rev-erbα in untreated and MG132-treated hippocampal neurons overexpressing Ophn1 siRNA or scrambled siRNA. Values are means ± s.e.m. * Figure 3: Altered circadian expression of Rev-erbα and clock gene mRNAs in Ophn1−/− mice. () Relative expression of Rev-erbα mRNA in hippocampus of wild-type and Ophn1−/− mice under dark/dark conditions at CT18, 0, 6 and 12. Samples were taken after 30 h (CT18), 36 h (CT0), 42 h (CT6) and 48 h (CT12) of continuous darkness, which started at 7 p.m. (CT0 was at 7 a.m.). Values are means ± s.e.m. of at least three experiments. *P < 0.05, ANOVA, Bonferroni post hoc test. () Rev-erbα protein expression in hippocampus and cortex of wild-type and Ophn1−/− mice at CT18, 0, 6 and 12. Total protein extract from wild-type and Ophn1−/− hippocampus was separated by SDS-PAGE and probed with antibody to Rev-erbα, then stripped and re-probed with antibody to oligophrenin-1 and tubulin. Histograms show the mean blot intensities (±s.e.m., at least three experiments) normalized to tubulin. At CT12 and CT18, Rev-erbα expression was significantly lower in Ophn1−/− than wild-type mice hippocampus homogenates. () Relative expression of Bmal1 mRNA in hippocampus an! d cortex of wild-type and Ophn1−/− mice under dark/dark conditions at CT18, 0, 6 and 12. Values are normalized to cyclophilin A mRNA and are ±s.e.m. of at least three experiments. () Data presented as in for cortex. () Data presented as in for cortex. () Data presented as in for cortex. * Figure 4: Accumulation of Rev-erbα in the dendrites and spines induced by overexpression of oligophrenin-1 in neurons. () In mature neurons (20 DIV), myc–Rev-erbα localized to nucleus, but it was also present in the cell body and dendrites (left). Overexpression of HA–oligophrenin-1 resulted in the accumulation of myc–Rev-erbα in dendrites and spines (middle). When HA–oligophrenin-1ΔC was overexpressed, there was no myc–Rev-erbα accumulation in dendrites and spines (right). Ophn1 siRNA abolished myc–Rev-erbα staining outside the nucleus (bottom panels). Scale bar represents 10 μm. () Histograms show the intensity of GFP- and myc-tagged Rev-erbα staining in dendrites and spines of cultured hippocampal neurons transfected as in . A.u., arbitrary units (average pixel intensity). Values are means ± s.e.m.; *P < 0.05, **P < 0.01, ***P < 0.001 (ANOVA, Tukey test). * Figure 5: Synaptic activity induces localization of Rev-erbα at synapses. () AMPA receptor and oligophrenin-1 mediated Rev-erbα localization in dendrites and dendritic spines. Hippocampal neurons were transfected with GFP and myc–Rev-erbα at 11 DIV and were treated for 1.5 h with the indicated drugs at 20 DIV. Bicuculline (40 μM), but not TTX (2 μM), induced localization of myc–Rev-erbα in dendrites and spines. Dendritic localization of myc–Rev-erbα in bicuculline-treated neurons was prevented by addition of NBQX (100 μM), but not by the addition of AP5 (100 μM). Application of bicuculline to neurons transfected with Ophn1 siRNA did not result in localization of myc–Rev-erbα to dendrites or spines. Scale bar represents 10 μm. () Histograms show the intensity of myc–Rev-erbα staining in dendrites and spines in neurons transfected as in . Bicuculline, but not TTX, resulted in major localization of myc–Rev-erbα to dendrites that was prevented by the addition of NBQX, but not AP5. Ophn1 siRNA abolished Rev-erbα localization to! dendrites even in the presence of bicuculline. A.u., arbitrary units (average pixel intensity). Values are means ± s.e.m. **P < 0.01, ***P < 0.001 (ANOVA, Tukey test). * Figure 6: Oligophrenin-1 is required for the synaptic activity–induced localization of endogenous Rev-erbα in dendrites. () Localization of endogenous Rev-erbα in mature hippocampal neurons (20 DIV). Endogenous staining of Rev-erbα in (left to right) untreated, bicuculline-treated (40 μM, 1.5 h), TTX-treated (2 μM, 1.5 h), bicuculline-treated (40 μM, 1.5 h) neurons overexpressing Ophn1 siRNA, and untreated neurons overexpressing GFP–oligophrenin-1. The lower right panel shows staining for oligophrenin-1 in oligophrenin-1–overexpressing neurons. Scale bar represents 10 μm. () Quantification of endogenous staining for Rev-erbα in dendrites of neurons treated as indicated in . Bicuculline treatment resulted in major localization of endogenous Rev-erbα in dendrites. TTX treatment and Ophn1 siRNA reduced Rev-erbα localization in dendrites even in the presence of bicuculline. Values are means ± s.e.m. *P < 0.05, **P < 0.01 (ANOVA, Tukey test). () Western blots quantifying (as ratio of density in dendrite to cell body) localization of Rev-erbα in dendrite fraction and soma fraction aft! er subcellular fractionation of untreated (NT) and bicuculline-treated (Bic) mature cultured hippocampal neurons (20 DIV). Values are means ± s.e.m. ##P < 0.01 (Student's t test). PSD95 is a synaptic marker; nuclear protein 4eBP1 was used as a soma marker. * Figure 7: Oligophrenin-1 interaction is required for Rev-erbα mRNA expression in hippocampal neurons. () Relative expression of Rev-erbα mRNA in bicuculline-treated (Bic) and untreated (NT) hippocampal neurons from wild-type and Ophn1−/− mice. Transcript levels were significantly higher in treated wild-type and untreated Ophn1−/− mice than in untreated wild-type mice. Data are presented as means ± s.e.m. (n = 3). *P < 0.05, **P < 0.01 (ANOVA, Bonferroni post test). () Relative expression of Rev-erbα mRNA in bicuculline-treated and untreated hippocampal neurons overexpressing either Ophn1 siRNA, scrambled siRNA, Ophn1 siRNA and an Ophn1 resistant to Ophn1 siRNA (siRNA rescue), or Ophn1 siRNA and an oligophrenin-1ΔC resistant to Ophn1 siRNA (siRNA rescue ΔC). Bicuculline-induced synaptic activity increased Rev-erbα transcript levels only in the presence of wild-type oligophrenin-1 (scrambled siRNA, siRNA-rescue) Values are means ± s.e.m. (n = 3). Change history * Abstract * Change history * Author information * Supplementary informationCorrected online 11 September 2011In the version of this article initially published online, affiliation 4 was misnumbered as 5, 5 was misnumbered as 6 and 6 was misnumbered as 4. The error has been corrected for the print, PDF and HTML versions of this article. Author information * Abstract * Change history * Author information * Supplementary information Affiliations * Consiglio Nazionale delle Ricerche (CNR) Institute of Neuroscience, Department of Pharmacology, University of Milan, Milan, Italy. * Pamela Valnegri, * Silvia Bassani, * Antonella Gianfelice, * Roberta Benfante, * Carlo Sala & * Maria Passafaro * Dulbecco Telethon Institute, Milan, Italy. * Pamela Valnegri, * Silvia Bassani & * Maria Passafaro * INSERM, U1016, Institut Cochin, Paris, France. * Malik Khelfaoui, * Olivier Dorseuil, * Celine Lagneaux, * Jamel Chelly & * Pierre Billuart * Centre National de la Recherche Scientifique (CNRS), UMR8104, Paris, France. * Malik Khelfaoui, * Olivier Dorseuil, * Celine Lagneaux, * Jamel Chelly & * Pierre Billuart * University of Paris Descartes, Paris, France. * Malik Khelfaoui, * Olivier Dorseuil, * Celine Lagneaux, * Jamel Chelly & * Pierre Billuart * Neuromuscular Diseases and Neuroimmunology, Neurological Institute and Foundation "Carlo Besta", Milan, Italy. * Carlo Sala Contributions P.V. conducted all the experiments in COS7 cells, in hippocampal neurons and in vivo. M.K., O.D. and C.L. conducted the experiments on circadian cycle. S.B. and A.G. prepared mutants for yeast two-hybrid screening. R.B. supervised the experiments with luciferase assays. J.C., P.B. and C.S. supervised the project. M.P. wrote the manuscript and supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Maria Passafaro Author Details * Pamela Valnegri Search for this author in: * NPG journals * PubMed * Google Scholar * Malik Khelfaoui Search for this author in: * NPG journals * PubMed * Google Scholar * Olivier Dorseuil Search for this author in: * NPG journals * PubMed * Google Scholar * Silvia Bassani Search for this author in: * NPG journals * PubMed * Google Scholar * Celine Lagneaux Search for this author in: * NPG journals * PubMed * Google Scholar * Antonella Gianfelice Search for this author in: * NPG journals * PubMed * Google Scholar * Roberta Benfante Search for this author in: * NPG journals * PubMed * Google Scholar * Jamel Chelly Search for this author in: * NPG journals * PubMed * Google Scholar * Pierre Billuart Search for this author in: * NPG journals * PubMed * Google Scholar * Carlo Sala Search for this author in: * NPG journals * PubMed * Google Scholar * Maria Passafaro Contact Maria Passafaro Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Change history * Author information * Supplementary information PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–5 Additional data - Retrieval-specific endocytosis of GluA2-AMPARs underlies adaptive reconsolidation of contextual fear
- Nat Neurosci 14(10):1302-1308 (2011)
Nature Neuroscience | Article Retrieval-specific endocytosis of GluA2-AMPARs underlies adaptive reconsolidation of contextual fear * Priyanka Rao-Ruiz1, 4 * Diana C Rotaru1, 2, 4 * Rolinka J van der Loo1 * Huibert D Mansvelder2 * Oliver Stiedl1, 3 * August B Smit1 * Sabine Spijker1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1302–1308Year published:(2011)DOI:doi:10.1038/nn.2907Received02 May 2011Accepted18 July 2011Published online11 September 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 Upon retrieval, fear memories are rendered labile and prone to modification, necessitating a restabilization process of reconsolidation to persist further. This process is also crucial for modulating both strength and content of an existing memory and forms a promising therapeutic target for fear-related disorders. However, the molecular and cellular mechanism of adaptive reconsolidation still remains obscure. Here we show that retrieval of fear memory induces a biphasic temporal change in GluA2-containing AMPA-type glutamate receptor (AMPAR) membrane expression and synaptic strength in the mouse dorsal hippocampus. Blockade of retrieval-induced, regulated, GluA2-dependent endocytosis enhanced subsequent expression of fear. In addition, this blockade prevented the loss of fear response after reconsolidation-update of fear memory content in the long-term. Thus, endocytosis of GluA2-containing AMPARs allows plastic changes at the synaptic level that exerts an inhibitory constr! aint on memory strengthening and underlies the loss of fear response by reinterpretation of memory content during adaptive reconsolidation. View full text Figures at a glance * Figure 1: Retrieval after contextual fear consolidation leads to endocytosis of AMPARs. () Experimental design with six groups of mice that, 24 h before a retrieval session, were exposed to the conditioned stimulus (CS) context only (no shock: NS-R), or received a shock (unconditioned stimulus) in the same context (US-R) or in a different context (US-RCB), or did not experience retrieval (NS-NR and US-NR). Mice were then analyzed 1 h after the retrieval session. The timeline for collection of dorsal hippocampi for immunoblot analysis (NS-R, n = 4 samples; US-R, n = 3; US-RCB, n = 4) is indicated. () Quantification of synaptic membrane fraction AMPA receptor subunits, as a percentage of NS-R values. Representative blots with samples that were compared on the same gel are shown (approximate molecular weight indicated; for input material used for normalization, see Supplementary Fig. 2). Downregulation of subunits of AMPARs 1 h after retrieval was observed exclusively as result of retrieval in the conditioning context (GluA1, F1,6 = 12.467; GluA2, F1,6 = 39.995; G! luA3, F1,6 = 10.122), but neither from consolidation alone nor from exposure to a novel context. All data points show mean ± s.e.m.; significant P-values are indicated. * Figure 2: Endocytosis of AMPARs is specific to retrieval of a conditioned fear memory. (–) Experimental design with three or with two groups that, 24 h before retrieval, were exposed to context only (NS-R), or received a shock either immediately upon placing in the box (immediate (imm) shock; IS-R) or after a delay (US-R). All groups received a retrieval session the next day, and 1 h later the dorsal hippocampi were collected for immunoblot analysis (n = 4 samples per condition). (,) Quantification of AMPA receptor subunits, as a percentage of the NS-R value. Representative blots with samples that were compared on the same gel are shown (approximate molecular weight indicated; for input material used for normalization see Supplementary Fig. 2). () AMPAR subunits from the synaptic membrane fraction were downregulated 1 h after retrieval (GluA1, F2,11 = 6.232; GluA2, F2,11 = 9.660; GluA3, F2,11 = 9.986), and this effect was not due to unspecific effects of the shock (immediate shock). () Left: normalized ratio of AMPA receptor subunits present on the surface t! o those present in the total homogenate, determined using biotinylation. This corroborated the downregulation of surface GluA2 (F1,7 = 10.441; Figs. 1b and 2b) compared with NS‐R, and concomitant reduction in AMPAR currents (Fig. 3). Right: example of no-biotin control before and after addition of NeutrAvidin beads for immune precipitation. Top, GluA2 immunodetection; bottom, Coomassie stain to control for input differences. GluA2 cannot be detected after immune precipitation, indicating the specificity of the method. All data points show mean ± s.e.m.; significant P-values are indicated. * Figure 3: Fast retrieval-induced decrease in synaptic strength in dorsal hippocampus. () Experimental design with four groups, in which mice, 24 h before the presence or absence of a retrieval session, were exposed only to the conditioned stimulus (CS; groups NS-R or NS-NR) or received a shock (US-R or US-NR). The timeline is indicated for collection of brains for in vitro slice physiology (n = 6 for NS-NR; n = 6 for US-NR; n = 4 for NS-R; n = 4 for US-R). (,) Representative recordings of AMPAR mEPSCs () and resulting averages of events superimposed (). () Cumulative frequency of mEPSC amplitudes, showing a significant (P < 0.0001) leftward shift in amplitude. () Bar graphs of AMPAR-mediated mEPSCs, showing decreased synaptic strength in shocked mice specifically 1 h after retrieval, without AMPAR current changes after conditioning. Number of individual cells measured are indicated. For cumulative frequencies, a Kolmogorov-Smirnov test (K‐S) was performed. All data points show mean ± s.e.m.; significant P-values are indicated. * Figure 4: A biphasic wave of synaptic AMPAR levels after retrieval translates into functional synaptic changes in dorsal hippocampus. () Experimental design with two groups, in which mice, 24 h before retrieval, were exposed only to the conditioned stimulus (CS; group NS-R), or received a shock (US-R). Timeline is shown for collection of dorsal hippocampi for immunoblot analysis (4 and 7 h; n = 4 samples per condition) and of brains for in vitro slice physiology (7 h; n = 10 for NS-R, n = 8 for US-R). () Quantification, as a percentage of NS-R value, of AMPAR subunits in the synaptic membrane fraction. Representative blots with samples that were compared on the same gel are shown (approximate molecular weight indicated; for input material used for normalization, see Supplementary Fig. 2), showing a continued downregulation of GluA2 (F1,7 = 60.951) and GluA3 (F1,7 = 10.824) 4 h after retrieval, and an increase in GluA2 expression (F1,7 = 36.65) 7 h after retrieval. (,) Representative recordings of AMPAR mEPSCs () and resulting averages of events superimposed () 7 h after retrieval, showing a change in decay! of AMPAR-mediated mEPSCs. () Cumulative frequency of mEPSC decay time, showing a significant (P < 0.001) leftward shift. For cumulative frequencies a Kolmogorov-Smirnov test (K-S) was performed. () Bar graphs of AMPAR-mediated mEPSCs, showing decreased decay time in shocked mice specifically 7 h after retrieval. Numbers of individual cells measured are indicated. (,) Temporal analysis of AMPAR-mediated mEPSCs, showing a biphasic wave of AMPAR regulation with decreased amplitudes 1 h after retrieval and increased amplitudes 7 h after retrieval, in the resulting averages of events () and in bar graphs representing AMPAR mEPSC amplitude (). All data points show mean ± s.e.m.; significant P-values are indicated. * Figure 5: AMPAR endocytosis is crucial for subsequent AMPAR membrane insertion 7 h after retrieval. () Experimental design with two main groups, in which mice, 24 h before retrieval, were exposed only to the conditioned stimulus (CS; group NS-R), or received a shock (US-R), and in which regulated endocytosis of GluA2-AMPARs was blocked by the peptide GluA23Y (3Y) or mice were treated with control peptide GluA23A (3A). Timeline is indicated for intervention (1 h before retrieval) and for collection (7 h after retrieval) of dorsal hippocampi for immunoblot analysis (n = 4 samples per condition) and brains for in vitro slice physiology (n = 8 NS-R; n = 4 US-3A-R; n = 5 US-3Y-R). (–) Preventing retrieval-induced regulated endocytosis of AMPARs attenuated subsequent upregulation of GluA2 at the molecular level (; F2,11 = 8.096; for input material, see Supplementary Fig. 2) and physiological level (). () Representative recordings of AMPAR mEPSCs. (,) Scaled and superimposed resulting averages () and cumulative frequency of decays () of AMPAR-mediated mEPSCs in the presence of ! the GluA23Y blocking peptide or the GluA23A control peptide. For cumulative frequencies a Kolmogorov-Smirnov test (K-S) was performed. () Group data of AMPAR-mediated mEPSC decay time. Numbers of individual cells measured are indicated. All data points show mean ± s.e.m.; significant P-values are indicated. * Figure 6: Retrieval-induced AMPAR endocytosis is crucial for modulating memory strength during reconsolidation. (–) Experimental design with two groups for the effect on reconsolidation of blocking regulated AMPAR endocytosis by dorsohippocampal injections of the GluA23Y peptide (3Y) and control GluA23A peptide (3A), showing timeline for training (T), testing using retrieval sessions (RT1–RT3), and dorsohippocampal injections, 1 h before retrieval (3Y-R, 3A-R, respectively) or 15 min after retrieval (3Y post-retrieval intervention, 3Y-PRI; ,), or 24 h after retrieval (,). (,) 3A-R, n = 10; 3Y-R, n = 11; 3Y-PRI, n = 6; (,) R-3A, n = 8; R-3Y, n = 8. () On days 2 and 3, both a pre- or post-retrieval intervention resulted in a facilitated fear response (increased freezing) with a significant effect of treatment (F2,24 = 6.980) and interaction of time × treatment (F2,24 = 4.178) over all three retrieval sessions (RT1–RT3). Freezing was affected in both the short term (RT2; F2,26 = 6.40) and the long term (RT3; F2,27 = 8.310). () Blocking regulated AMPAR endocytosis outside the windo! w of reconsolidation had no effect on freezing on the subsequent day (day 4), in contrast to blocking endocytosis within the reconsolidation window (see ,). All data points show mean ± s.e.m.; significant P-values are indicated. * Figure 7: Retrieval-induced AMPAR endocytosis mediates attenuation of fear memory expression by reconsolidation update. (–) Experimental design for testing the effect on reconsolidation update of the timing of a pre-extinction retrieval session (,) and the effect of blocking regulated AMPAR endocytosis by dorsohippocampal injections of GluA23Y (3Y) or control GluA23A (3A) peptide (,), showing timeline for training (T), intervention and testing using retrieval sessions (RT1–RT3) or extinction (Ext1–10, a 30-min extinction session divided into 10 bins of 3 min each). LTM, long-term memory test of extinction; SRT, spontaneous recovery test. (,) R-E 2 h, n = 10; NR-E, n = 10; R-E 24 h, n = 8; (,) all n = 5. (,) All groups acquired extinction similarly (Supplementary Fig. 6), reached the same levels of freezing in the last 3 min (Ext10) of the 30-min session, and showed similar levels of freezing in the LTM test. () An effect of time (F1,25 = 15.072) and time × group (F1,25 = 4.426) was observed for groups between the LTM test on day 3 and SRT on day 17. A significant difference between gro! ups was observed (SRT; F2,27 = 5.175), with the NR-E and R-E 24 h groups showing spontaneous recovery of fear, and R-E 2 h showing prevention of return of fear. () Treatment had an effect in the first 3 min (Ext1; F1,9 = 10.01) consistent with the acute effect on reconsolidation (Fig. 6). An effect of treatment (F1,9 = 2.50) and treatment × time (F1,9 = 9.06) was observed for groups between the LTM test on day 3 and SRT test on day 17. A significant difference between groups was observed (SRT; F1,9 = 7.70), with GluA23Y groups showing spontaneous recovery of fear, whereas controls (GluA23A) showed a long-term loss of fear response. All data points show mean ± s.e.m.; significant P-values are indicated. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Priyanka Rao-Ruiz & * Diana C Rotaru Affiliations * Department of Molecular & Cellular Neurobiology, Center for Neurogenomics & Cognitive Research, Neuroscience Campus Amsterdam, VU University (Vrije Universiteit), Amsterdam, The Netherlands. * Priyanka Rao-Ruiz, * Diana C Rotaru, * Rolinka J van der Loo, * Oliver Stiedl, * August B Smit & * Sabine Spijker * Department of Integrative Neurophysiology, Center for Neurogenomics & Cognitive Research, Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands. * Diana C Rotaru & * Huibert D Mansvelder * Department of Functional Genomics, Center for Neurogenomics & Cognitive Research, Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands. * Oliver Stiedl Contributions P.R.-R., A.B.S. and S.S. designed the molecular experiments. P.R.-R., D.C.R., H.D.M. and S.S. designed the physiological experiments. P.R.-R., O.S. and S.S. designed the behavioral experiments. P.R.-R. executed molecular experiments. D.C.R. executed physiological experiments. P.R.-R. and R.J.v.d.L. executed behavioral experiments. P.R.-R. and S.S. analyzed molecular experiments. D.C.R. and H.D.M. analyzed physiological experiments. P.R.-R. and S.S. analyzed behavioral experiments. P.R.-R., D.C.R., A.B.S. and S.S. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Sabine Spijker Author Details * Priyanka Rao-Ruiz Search for this author in: * NPG journals * PubMed * Google Scholar * Diana C Rotaru Search for this author in: * NPG journals * PubMed * Google Scholar * Rolinka J van der Loo Search for this author in: * NPG journals * PubMed * Google Scholar * Huibert D Mansvelder Search for this author in: * NPG journals * PubMed * Google Scholar * Oliver Stiedl Search for this author in: * NPG journals * PubMed * Google Scholar * August B Smit Search for this author in: * NPG journals * PubMed * Google Scholar * Sabine Spijker Contact Sabine Spijker Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (573K) Supplementary Figures 1–7 Additional data - Cone photoreceptor contributions to noise and correlations in the retinal output
- Nat Neurosci 14(10):1309-1316 (2011)
Nature Neuroscience | Article Cone photoreceptor contributions to noise and correlations in the retinal output * Petri Ala-Laurila1, 3 * Martin Greschner2, 3 * E J Chichilnisky2 * Fred Rieke1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1309–1316Year published:(2011)DOI:doi:10.1038/nn.2927Received29 March 2011Accepted11 August 2011Published online18 September 2011 Abstract * Abstract * Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Transduction and synaptic noise generated in retinal cone photoreceptors determine the fidelity with which light inputs are encoded, and the readout of cone signals by downstream circuits determines whether this fidelity is used for vision. We examined the effect of cone noise on visual signals by measuring its contribution to correlated noise in primate retinal ganglion cells. Correlated noise was strong in the responses of dissimilar cell types with shared cone inputs. The dynamics of cone noise could account for rapid correlations in ganglion cell activity, and the extent of shared cone input could explain correlation strength. Furthermore, correlated noise limited the fidelity with which visual signals were encoded by populations of ganglion cells. Thus, a simple picture emerges: cone noise, traversing the retina through diverse pathways, accounts for most of the noise and correlations in the retinal output and constrains how higher centers exploit signals carried by par! allel visual pathways. View full text Figures at a glance * Figure 1: Covariation of excitatory inputs to cells that share little known circuitry. () Simultaneous recordings of excitatory synaptic inputs to an ON and an OFF parasol ganglion cell during modulated and constant light (top trace, 50% contrast, mean of 4,000 R* per cone per s). Holding potentials were ~−70 mV. () Crosscorrelation functions for the excitatory synaptic inputs measured during constant light (excluding 500 ms following the end of modulate light period; see Online Methods) from the same cell pair as (left) and averaged across nine cell pairs (right, mean ± s.e.m.). (,) Correlations in excitatory synaptic inputs to ON midget and ON parasol ganglion cells as in and (n = 18). (,) Correlations in excitatory synaptic inputs to horizontal cells and ON parasol ganglion cells as in and (n = 5). Recordings shown in – were in flat-mount preparations, and those shown in and were in slice preparations. * Figure 2: Effect of APB and mixture of LY341495 and APB on light responses of ON parasol ganglion cells. () Example of titration of a mixture of LY341495 and APB to match the holding current without drugs. Open circles plot current in constant light (4,000 R* per cone per s) while holding the cell near the reversal potential for inhibitory synaptic input. The cell was superfused with solutions containing 7.5 μM LY341495 and 2.5 μM APB (ratio 1), 7.5 μM LY341495 and 5 μM APB (ratio 2), and 10 μM APB. () Excitatory synaptic inputs to an ON parasol cell elicited by a decrement in light intensity from 4,000 to 0 R* per cone per s for 500 ms. Increases in light intensity generated large excitatory inputs in control conditions. APB decreased the holding current by suppressing tonic excitatory input, eliminated the response to increases in light intensity and unmasked a large response to decreases in light intensity. A mixture of LY341495 and APB (LY/APB) almost entirely suppressed increases in excitatory input for both decreases and increases in light intensity while also matchi! ng the holding current in control conditions. Much of the current change remaining in LY/APB-treated cells likely reflects OFF pathway–derived presynaptic inhibition, which decreases bipolar cell glutamate release. () Collected data from six cells as in , plotting the maximum (mean ± s.e.m.) light-evoked inward current at light onset (ON) and offset (OFF). * Figure 3: Correlated and total noise in ganglion cell excitatory synaptic inputs are dominated by cone noise. () Simultaneous recordings of excitatory synaptic input to an ON parasol (top) and an ON midget (bottom) ganglion cell before (left) and during (right) superfusion with a mix of 7.5 μM LY341495 and 4 μM APB. Dashed lines show the mean current level in constant light (4,000 R* per cone per s) before exposure to the drugs. () Crosscorrelation functions measured during constant light before (black) and in the presence of (gray) LY/APB for the same cell pair shown in . () Peak crosscorrelation before LY/APB exposure plotted against that in the presence of LY/APB for six ON parasol/ON midget cell pairs. Also shown are peak crosscorrelations for five OFF parasol pairs as a control; correlations were measured from the residuals during modulated light to minimize the effects of nonlinearities in the OFF circuitry (see Online Methods and ref. 26). () Current variance from 0–100 Hz measured in ON parasol ganglion cells during control conditions plotted against that in the presence! of LY/APB (including some recordings from single cells not shown in ). () Mean currents during control conditions and in the presence of LY/APB for the cells shown in . The mean current in control conditions was 1.05 ± 0.10 (mean ± s.e.m.)-fold larger than that in the presence of LY/APB. * Figure 4: Rapid fluctuations in cone voltage are conveyed to ganglion cells. () Cone voltage fluctuates more rapidly than the light response. A brief section of voltage fluctuations during constant light (top) and average response to a 10-ms flash (bottom) of a current-clamped cone are shown. Recordings were made at a mean light level of 4,000 R* per cone per s. () Left, autocorrelation function of noise and light response for the cone shown in . Right, average autocorrelation functions for five cones. For comparison, the autocorrelation function of the light-evoked response of ON parasol cells is also plotted; its narrower width when compared with the cone light response indicates substantial high-pass filtering in the retinal circuitry. () Measurement of kinetics of signal transfer from cones to ganglion cells. The voltage of a single cone was modulated randomly while measuring the resulting variations in excitatory synaptic input to an ON parasol ganglion cell. The cone voltage modulations shown have been filtered to make the slower modulations mo! re apparent. The kinetics of signal transfer were estimated by calculating the filter that provides the best linear estimate of the ganglion cell currents given the cone voltage (right). () Average filters for paired recordings between cones and OFF parasol cells (n = 4) and cones and ON parasol cells (n = 4). The filters predicting the ganglion cell currents from the light inputs are shown for comparison (based on nine recordings from ON/OFF parasol pairs). The opposite polarities of the coneRGC and lightRGC filters are expected because increases in light input hyperpolarize rather than depolarize the cones. * Figure 5: Dendritic overlap predicts correlation strength. () Maximum-points projections of confocal images of parasol (top left) and midget (bottom left) ganglion cells. The images cover the same region of space, but have been separated for clarity. Right, discretized regions of the dendrites with a model of the cone array (open red circles) overlaid. The weight of a given cone input to each ganglion cell was estimated from the length of dendrite in an area around the cone determined by the size of the axon terminals of the diffuse and midget cone bipolar cells (shaded red regions) that convey cone signals to parasol and midget ganglion cells. () Midget-parasol ganglion cell pairs with low (left) and high (right) dendritic overlap. () Relationship between measured strength of correlated variability in excitatory inputs to midget and parasol ganglion cell pairs (as in Fig. 1) and predicted correlation based on the model outlined in . Open circles show the same analysis for ON parasol pairs. Lines indicate the expected dependence of ! correlation strength on overlap for models with different ratios of shared and independent noise as indicated in the labels (shared:independent). Recordings were made at a mean light level of 4,000 R* per cone per s. * Figure 6: Shared noise limits fidelity of neural coding in populations of ganglion cells. () Ovals in the top panel represent Gaussian approximation of the receptive fields of simultaneously recorded ON parasol cells. Pairs of parasol cells were used to reconstruct a time-varying, spatially uniform light input (gray trace, bottom). The spike response of each cell was convolved with an appropriate linear filter and the output summed to generate the reconstruction (black trace). Cell pairs in red and blue are highlighted in and . () Dependence of SNR of the reconstruction on distance between the two cells, measured in units of the receptive field radius (sd, see top panel in ). The SNR was normalized so that pairs of cells that sample independent noise reached a value of 1, as determined in distant cell pairs. Neighboring cell pairs are in black. Red and blue points represent cell pairs in . The two panels are two different preparations. (–) Data are presented as in for combinations of parasol and midget cells in two preparations. () Dependence of SNR on receptiv! e field overlap for ON parasol pairs. Lines indicate the expected dependence of SNR on overlap for models with different ratios of shared to independent noise. Closed symbols show overlap measured by correlating each pixel of raw receptive field measurements; open symbols show overlap estimated from Gaussian receptive field fits. Red and blue points represent cell pairs in . Circles and squares represent different preparations from . The mean light level was 1,200 R* per cone per s. * Figure 7: Dependence of predicted correlation strength on model parameters. Each panel compares predicted and measured correlation strength as in Figure 5c. () Model in which bipolar cells are arranged on a grid and cones provide input to closest bipolar cells. All other parameters are as described in Figure 5c. () Model in which bipolar cells spread signals over a 81 (27) μm radius disc for the diffuse (midget) cone bipolar cells. () Model in which bipolar signal spread was 13.5 (4.5) μm for the diffuse (midget) cone bipolar cells. Author information * Abstract * Author information Primary authors * These authors contributed equally to this work. * Petri Ala-Laurila & * Martin Greschner Affiliations * Howard Hughes Medical Institute and Department of Physiology and Biophysics, University of Washington, Seattle, Washington, USA. * Petri Ala-Laurila & * Fred Rieke * The Salk Institute, La Jolla, California, USA. * Martin Greschner & * E J Chichilnisky Contributions P.A.-L., M.G., E.J.C. and F.R. designed and performed experiments. M.G. and F.R. analyzed data. P.A.-L., M.G., E.J.C. and F.R. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Fred Rieke Author Details * Petri Ala-Laurila Search for this author in: * NPG journals * PubMed * Google Scholar * Martin Greschner Search for this author in: * NPG journals * PubMed * Google Scholar * E J Chichilnisky Search for this author in: * NPG journals * PubMed * Google Scholar * Fred Rieke Contact Fred Rieke Search for this author in: * NPG journals * PubMed * Google Scholar Additional data - Coordinated dynamic encoding in the retina using opposing forms of plasticity
- Nat Neurosci 14(10):1317-1322 (2011)
Nature Neuroscience | Article Coordinated dynamic encoding in the retina using opposing forms of plasticity * David B Kastner1 * Stephen A Baccus2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1317–1322Year published:(2011)DOI:doi:10.1038/nn.2906Received25 May 2011Accepted12 July 2011Published online11 September 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 range of natural inputs encoded by a neuron often exceeds its dynamic range. To overcome this limitation, neural populations divide their inputs among different cell classes, as with rod and cone photoreceptors, and adapt by shifting their dynamic range. We report that the dynamic behavior of retinal ganglion cells in salamanders, mice and rabbits is divided into two opposing forms of short-term plasticity in different cell classes. One population of cells exhibited sensitization—a persistent elevated sensitivity following a strong stimulus. This newly observed dynamic behavior compensates for the information loss caused by the known process of adaptation occurring in a separate cell population. The two populations divide the dynamic range of inputs, with sensitizing cells encoding weak signals and adapting cells encoding strong signals. In the two populations, the linear, threshold and adaptive properties are linked to preserve responsiveness when stimulus statistics ! change, with one population maintaining the ability to respond when the other fails. View full text Figures at a glance * Figure 1: Adaptation and sensitization in separate neural populations. () Stimulus intensity alternating between high and low contrast during a single trial (top), for salamander (left) and mouse (right). Firing rate response for adapting (middle) and sensitizing (bottom) cells, averaged over all trials, each with a different stimulus sequence. Color indicates response to low contrast; gray, to high contrast. () Average time to first spike after a transition from high to low contrast (n = 2–12 cells). () Nonlinearities of a linear-nonlinear model (see Online Methods) for cells in calculated during intervals indicated by bars in for salamander (left) and mouse (right). The interval Learly was defined as 0.5–2 s after the transition to low contrast, and Llate was 10–16 s for salamander and 10–15 s for mouse. () Adaptive indices (see Online Methods) for 190 ganglion cells from 16 salamander retinas. The distribution is significantly bimodal (Hartigan's dip test, P < 0.05). () High contrast (35%) was presented for 1, 2 or 5 s, followed by l! ow contrast (3%) for 15 s. The average change in firing rate between Learly and Llate is shown normalized by the average rate for low contrast in all conditions (n = 5 cells). Black line is an exponential fit to the data. () For the same cells, the adaptive index computed separately for changing contrast at a fixed mean luminance (abscissa) and compared to the adaptive index when changing the mean luminance by a factor of 16 at a fixed contrast of 10% (ordinate) (see Supplementary Fig. 4). Error bars in ,, s.e.m. over cells. * Figure 2: Sensitizing and adapting populations encode common stimulus features. () Average response of salamander adapting and sensitizing cells to 26 trials of the same stimulus repeated during Learly and Llate after 4 s of high contrast (35%). Low contrast was 3–5%. Firing rate binned at 10 ms. () Absolute difference in time between events in all pairs of fast OFF-type adapting cells (n = 28) and sensitizing cells (n = 12). Events defined as times when a cell's firing rate, binned at 10 ms, exceeded 20 Hz. () Average temporal (top) and spatial (bottom) filters for adapting (n = 142) and sensitizing (n = 48) fast OFF cells, mapped in one dimension. Curves obscure the error bars (s.e.m.) located at the peak and trough of the temporal filters and along the spatial filters. Spatial filters normalized to their peaks. () Fractions of adapting and sensitizing cells of different cell types, as classified by a cell's temporal filter (n = 209 fast OFF, 16 medium OFF, 20 slow OFF, 9 ON) (Supplementary Fig. 10). () Spatial receptive field centers of fast OFF ad! apting and sensitizing cells recorded simultaneously. Receptive fields displayed at 1 s.d. of a two-dimensional Gaussian fit. () Histogram of spacing (see Online Methods) between nearest neighbors of fast OFF adapting (n = 615) and sensitizing cells (n = 171). * Figure 3: Improvement of discriminability in a combined population of sensitizing and adapting cells. () Nonlinearities for adapting (n = 21) and sensitizing (n = 13) cells during Learly (left) and Llate (right). () Discriminability between nearby stimuli, d′(g), as a function of the filtered stimulus g (see Online Methods) in the full population minus d′(g) for the adapting population alone (blue) or minus d′(g) for the sensitizing population alone (red) during Learly (left) and Llate (right). All values were normalized by the area of the total d′ in the full population during Llate. * Figure 4: Sensitizing cells specialize to encode weak signals; adapting cells encode strong signals. () Twelve different contrast levels (3–36%) were randomly interleaved for at least 110 s and three repeats, and the first 10 s of data in each contrast was discarded. Nonlinearities are shown for an adapting (top) and sensitizing (bottom) cell for the different contrasts. Each row is a different nonlinearity, displayed in a color scale. Black dots indicate 1 s.d. above the mean for each contrast level. Nonlinearities calculated from the data (left) and as predicted using the model described in panel (right). () Normalized nonlinearities from cells in panel . For each contrast, the nonlinearity was scaled along the abscissa by the input s.d. (top) or shifted by a common factor (α) and then scaled along the abscissa by the contrast (bottom). () Model Mα. Input values were passed through a threshold function, which shifted the mean value by a factor α, then were rescaled by the contrast (σ) and then passed through a secondary nonlinearity with threshold θ to recreate the! range of nonlinearities shown in . The secondary nonlinearity is the average nonlinearity for a cell after shifting by α and rescaling. () Nonlinearities Ni (g) were computed for each 3 s bin. For each bin, an estimate of the contrast was determined as the contrast σ for which the steady-state nonlinearity of the model Mα (σ) had the smallest mean-squared difference from Ni (g). Low contrast (5%) followed 40 s of high contrast (35%). * Figure 5: Sensitizing and adapting cells increase information transmission using opposing changes in firing rate. () Stimulus used in the calculation of mutual information and the stimulus specific information (SSI) for low contrast. Identical high-contrast flashes for 20 s were followed by Learly, which was 2 s of eight randomly presented low-contrast flashes (different colors) having a Michelson contrast, (Imax−Imin)/(Imax+Imin), that ranged from 1% to 8%. High contrast was 100% Michelson contrast. For Llate, every 180 s, 44 s of continuous, randomly organized, low-contrast flashes were presented. () Mutual information during Llate versus Learly. Llate occurred from 22 to 44 s after the end of high contrast, and Learly occurred from 0.5 to 2 s after the end of high contrast. All sensitizing cells had a higher firing rate during Learly than Llate (Supplementary Fig. 9a). Bin size is 150 ms, but the increase of information during low contrast was independent of bin size (Supplementary Fig. 9b). () Average mean and variance during Learly (lighter colors, thicker lines) and Llate (darke! r colors, thinner lines) for the sensitizing cells in , shown as a function of the stimulus flashes amplitude. () Stimulus specific information ISSI for each of the eight different low-contrast stimuli. In and , flash amplitude is the Michelson contrast of the eight brief flashes in the low-contrast stimulus. Error bars in ,, s.e.m. over cells. * Figure 6: Model of sensitization. () Sensitization results from the difference between two adapting pathways, one excitatory and one inhibitory. In each pathway, the stimulus, s, is passed through a linear filter, L, a threshold, N, and then an adapting block, A. The adapting block is a feedforward module. In the inhibitory pathway, the input u(t) is convolved with an exponential filter, FA yielding FA*u (see Online Methods). The input u(t) is then divided by the filtered input FA*u, such that the output of the adapting block v(t) has a smaller amplitude than the input u(t). A temporal filter, LQ, and saturating function, NQ, are applied to the inhibitory pathway before the two pathways are combined. () Response of the model to an input that repeated, and was identical during Learly and Llate. () Average responses over many white noise sequences, shown at different stages in the model. In the inhibitory pathway at point v, the response decreases during high contrast (gray) and recovers during low contrast (b! lue). The synaptic functions decrease the response modulation during high contrast (w). The decrease in inhibition at the transition to low contrast elevates activity in the excitatory pathway (y). The final adapting block, AE, in the excitatory pathway yields adaptation during high contrast and preserves sensitization during low contrast (z). Author information * Abstract * Author information * Supplementary information Affiliations * Neuroscience Program, Stanford University School of Medicine, Stanford, California, USA. * David B Kastner * Department of Neurobiology, Stanford University School of Medicine, Stanford, California, USA. * Stephen A Baccus Contributions D.B.K. and S.A.B. designed the study, D.B.K. performed the experiments and analysis, and D.B.K. and S.A.B. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Stephen A Baccus Author Details * David B Kastner Search for this author in: * NPG journals * PubMed * Google Scholar * Stephen A Baccus Contact Stephen A Baccus 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 Figures 1–10 Additional data - Constructing scenes from objects in human occipitotemporal cortex
- Nat Neurosci 14(10):1323-1329 (2011)
Nature Neuroscience | Article Constructing scenes from objects in human occipitotemporal cortex * Sean P MacEvoy1, 2 * Russell A Epstein2 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1323–1329Year published:(2011)DOI:doi:10.1038/nn.2903Received29 March 2011Accepted06 July 2011Published online04 September 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 We used functional magnetic resonance imaging (fMRI) to demonstrate the existence of a mechanism in the human lateral occipital (LO) cortex that supports recognition of real-world visual scenes through parallel analysis of within-scene objects. Neural activity was recorded while subjects viewed four categories of scenes and eight categories of 'signature' objects strongly associated with the scenes in three experiments. Multivoxel patterns evoked by scenes in the LO cortex were well predicted by the average of the patterns elicited by their signature objects. By contrast, there was no relationship between scene and object patterns in the parahippocampal place area (PPA), even though this region responds strongly to scenes and is believed to be crucial for scene identification. By combining information about multiple objects within a scene, the LO cortex may support an object-based channel for scene recognition that complements the processing of global scene properties in the! PPA. View full text Figures at a glance * Figure 1: Experimental stimuli. Subjects viewed 104 scene images drawn from four categories (kitchen, bathroom, playground and roadway intersection) and 208 object images drawn from eight categories strongly associated with the scenes (refrigerators and stoves for kitchens, toilets and bathtubs for bathrooms, swings and slides for playgrounds, and traffic signals and cars for intersections). Each scene contained the two corresponding signature objects; however, none of the object exemplars was drawn from any of the scene exemplars. * Figure 2: Logic of scene classification analysis. Scene patterns evoked by actual scenes in one half of scans were compared to predictor patterns derived from object-evoked patterns from the opposite half. Activity maps shown are actual scene-evoked patterns (top) and the averages of object-evoked patterns (bottom) for one subject. Correct scene-from-object classification decisions occurred when actual scene patterns were more similar to predictors that are based on their own associated objects than to the predictors that are based on objects from other scene contexts. * Figure 3: Multivoxel classification of scenes using object-based predictors. Results for each experiment are plotted separately for each ROI. Scene classification accuracy using the mean and single-object predictors was significantly above chance in LO in experiments 1, 2 and 3 (E1, E2 and E3, respectively). Accuracy was not above chance in any experiment in either pF or PPA. Furthermore, performance in LO was higher for the mean predictor than for the single-object predictor in each experiment (P < 0.05; see Results). Error bars indicate s.e.m. **P < 0.01. * Figure 4: Classification of objects on the basis of the patterns elicited by their same-context counterpart objects (for example, the accuracy of discriminating refrigerators from bathtubs on the basis of patterns evoked by stoves and toilets). Accuracy was significantly above chance in LO in experiment 1 (E1), but not above chance in any ROI in experiment 2 (E2) or experiment 3 (E3). Error bars indicate s.e.m. ***P < 0.001. * Figure 5: Group random-effects analysis of local searchlight accuracy maps for the classification of scenes from object averages, including subjects from all three experiments. Painted voxels represent centers of searchlight clusters with above-chance classification accuracies (P < 0.005, uncorrected). Displayed slices are cardinal planes containing the occipitotemporal voxel of peak significance, which was found in the left hemisphere (LH). Outlined regions are LO (dark blue), pF (light blue) and the PPA (green), which are defined from random-effects analysis of volumes across subjects (P < 0.00001, uncorrected). Although pF and PPA overlap when defined using these group data, they did not overlap when defined at the individual subject level. The apparent bias toward higher performance in left LO is addressed in the Supplementary Results and Supplementary Figure 7. RH, right hemisphere. * Figure 6: Behavioral evidence for object-based scene recognition. () Subjects saw briefly presented exemplars of scenes from each of the four categories used in the fMRI studies and performed a four-alternative forced-choice scene identification task. Each exemplar was shown intact (left) or with one (middle) or both (right) of its signature objects obscured. () Average accuracy (left) and response time (right) are shown for images with zero, one or two objects removed. NM, no mask (zero objects removed). Data for conditions with objects removed are shown for three different ranges of the percentage of image pixels removed. For matched percentages of pixel deletion, accuracy and reaction time were significantly degraded when both signature objects were removed compared to when just one was removed; this effect was only significant when a high percentage of the scene pixels were deleted, which is likely to correspond to the range in which image-based identification falters. Accuracy and response time estimates are from application of the Jo! hnson–Neyman procedure, which does not produce error bars. *P < 0.05 and ***P < 0.001. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Psychology, Boston College, Chestnut Hill, Massachusetts, USA. * Sean P MacEvoy * Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, USA. * Sean P MacEvoy & * Russell A Epstein Contributions S.P.M. and R.A.E. designed the experiments. S.P.M. collected fMRI data and R.A.E. collected behavioral data. S.P.M. analyzed data with input from R.A.E. S.P.M. and R.A.E. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Sean P MacEvoy Author Details * Sean P MacEvoy Contact Sean P MacEvoy Search for this author in: * NPG journals * PubMed * Google Scholar * Russell A Epstein Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (393K) Supplementary Figures 1–7, Supplementary Results Additional data - Grid cells generate an analog error-correcting code for singularly precise neural computation
- Nat Neurosci 14(10):1330-1337 (2011)
Nature Neuroscience | Article Grid cells generate an analog error-correcting code for singularly precise neural computation * Sameet Sreenivasan1, 2 * Ila Fiete1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1330–1337Year published:(2011)DOI:doi:10.1038/nn.2901Received15 March 2011Accepted18 July 2011Published online11 September 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 Entorhinal grid cells in mammals fire as a function of animal location, with spatially periodic response patterns. This nonlocal periodic representation of location, a local variable, is unlike other neural codes. There is no theoretical explanation for why such a code should exist. We examined how accurately the grid code with noisy neurons allows an ideal observer to estimate location and found this code to be a previously unknown type of population code with unprecedented robustness to noise. In particular, the representational accuracy attained by grid cells over the coding range was in a qualitatively different class from what is possible with observed sensory and motor population codes. We found that a simple neural network can effectively correct the grid code. To the best of our knowledge, these results are the first demonstration that the brain contains, and may exploit, powerful error-correcting codes for analog variables. View full text Figures at a glance * Figure 1: Coding for location: GPC and CPCs. () The general problem of (noisy channel) coding. A variable x is represented in some way, as but the representation is subject to noise with a given distribution. The problem is to find efficient representations that allow an ideal observer to most accurately estimate x. () Top, schematic spatial tuning curves of three grid cells (light gray, gray and black) with identical response periods (λα) and different preferred phases Bottom, current animal location. Right, the instantaneous error-free firing rates of all grid cells in one network, arranged by preferred phase, encode current animal location as an instantaneous network phase; the error-free instantaneous network phase is () Top, N independent M-neuron networks, encoding N instantaneous phases. Each phase has ~M coding states. Together, the networks provide ~MN coding states. Bottom, one CPC network of NM neurons, with unimodal tuning curves, encodes location as a single phase with a resolution of ~NM states. () Firi! ng rates of three random cells, from the CPC network (bottom) or from three different grid networks (top), as x is varied. () Sample of (ideally) decoded locations obtained from the slightly perturbed phases for the GPC (black) and CPC (green), for true location x0. Noise (inset) is Gaussian. RCPC = R = 90,090 cm, N = 5, σα = 0.04, = {10, 14, 18, 22, 26} cm. Parameters are common to and . () Data are generated as in , but encoding and decoding are restricted to an interval of size Rl<displacement), even though individual phase errors grow slowly. Green, CPC; black, grid cell–readout–grid cell closed-loop continuous operation. Inset, log-log plot; envelopes indicate where 25–75% and 10–90% of all 100 trials lie, respectively. The ratio of slopes (diffusion constants) of CPC and closed-loop trajectories was ≈ 104 in . Data in are generated as in , but with fivefold larger amplitude phase noise. Phase noise in one error-correcting iteration frequently exceeded dmin/2, so error correction failed (black). Blue, readout with continuity constraint. The ratio of slopes for the CPC and closed-loop grid cell–readout–grid cell network with continuity constraint was ≈ 5 × 104. See Online Methods for parameters. Author information * Abstract * Author information * Supplementary information Affiliations * Center for Learning and Memory, University of Texas at Austin, Austin, Texas, USA. * Sameet Sreenivasan & * Ila Fiete * Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York, USA. * Sameet Sreenivasan Contributions I.F. conceived the model. S.S. performed simulations and analyzed the data. I.F. and S.S. performed analytical calculations and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ila Fiete Author Details * Sameet Sreenivasan Search for this author in: * NPG journals * PubMed * Google Scholar * Ila Fiete Contact Ila Fiete Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (934K) Supplementary Results Additional data - Medial prefrontal cortex as an action-outcome predictor
- Nat Neurosci 14(10):1338-1344 (2011)
Nature Neuroscience | Article Medial prefrontal cortex as an action-outcome predictor * William H Alexander1 * Joshua W Brown1 * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:1338–1344Year published:(2011)DOI:doi:10.1038/nn.2921Received18 January 2011Accepted20 July 2011Published online18 September 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 medial prefrontal cortex (mPFC) and especially anterior cingulate cortex is central to higher cognitive function and many clinical disorders, yet its basic function remains in dispute. Various competing theories of mPFC have treated effects of errors, conflict, error likelihood, volatility and reward, using findings from neuroimaging and neurophysiology in humans and monkeys. No single theory has been able to reconcile and account for the variety of findings. Here we show that a simple model based on standard learning rules can simulate and unify an unprecedented range of known effects in mPFC. The model reinterprets many known effects and suggests a new view of mPFC, as a region concerned with learning and predicting the likely outcomes of actions, whether good or bad. Cognitive control at the neural level is then seen as a result of evaluating the probable and actual outcomes of one's actions. View full text Figures at a glance * Figure 1: The PRO model. () In an idealized experiment, a task-related stimulus (S) signaling the onset of a trial is presented. Over the course of a task, the model learns a timed prediction (V) of possible responses and outcomes (r). The temporal difference learning signal (δ) is decomposed into its positive and negative components (ωP and ωN, respectively), indicating unpredicted occurrences and unpredicted non-occurrences, respectively. () ωN accounts for typical effects observed in mPFC from human imaging studies. Conflict and error likelihood panels show activity magnitude aligned on trial onset; error and error unexpectedness panels show activity magnitude aligned on feedback. Model activity (vertical axis) is in arbitrary units. HEL, high error likelihood; LEL, low error likelihood. Error bars indicate s.e.m. Contrasts indicate the difference in model activity between two conditions. () Typical time courses for components of the PRO model. * Figure 2: ERP simulations. () Left: simulated fERN difference wave. Effects of surprising outcomes (low error likelihood,error – high error likelihood,correct) were larger than outcomes that were predictable (high error likelihood,error minus low error likelihood,correct). Right: observed ERP difference wave, adapted with permission from ref. 31, consistent with simulation results. ("Hard" and "easy" indicate task difficulty). () The effects of speed–accuracy tradeoffs on ERP amplitude are observed in the PRO model (left). Trials for incongruent (incong.) and congruent (cong.) conditions were divided into quintile bins by reaction time (large markers, slow reaction times; small markers, fast reaction times), and activity of the PRO model was calculated for correct trials in each bin. Accuracy and activity of the model were highest for trials with long reaction times and lowest for trials with short reaction times, consistent with human EEG data (right; adapted with permission from ref. 33)! . () The simulated activity of the PRO model (left) reflects amplitude and duration of the N2 component observed in humans EEG studies (right; aligned on stimulus onset (Stim); adapted with permission from ref. 33). Model activity (vertical axis) is in arbitrary units. * Figure 3: Single-unit neurophysiology simulation. () Calculation of the negative surprise signal ωN was performed for individual outcome predictions (indexed as i). For predictions of, for example, reward, the surprise signal increases steadily to the time at which the reward is predicted. The signal is suppressed on the occurrence of the predicted reward. Single units predicting error follow a similar pattern, with increased variance in the timing of the error. () The complement of negative surprise (namely, positive surprise ωP) indicates unpredicted occurrences. Model activity (vertical axis) is in arbitrary units. () Reward-predicting and reward-detecting cells recorded in monkey mPFC consistent with simulation results. Top: activity of a single unit consistent with the prediction of a reward. On error trials, activity peaks and gradually attenuates, potentially signaling an unsatisfied prediction of reward. Bottom: single-unit activity related to the detection of a rewarding event. Adapted with permission from ref. 2! 8. * Figure 4: fMRI simulations. Except where noted below, vertical axes represent model activity in arbitrary units. Contrasts indicate the difference in model activity between two conditions. () Multiple response effects. The change signal task is modified to require both change and go responses simultaneously when a change signal cue is presented. Change trials lead to greater prediction layer activity (aligned on trial onset) compared with go trials, even though response conflict is by definition absent. The incongruency effect in the absence of conflict is the multiple response effect23. () Volatility effects. When environmental contingencies change frequently (Volatile 1 & 2), mPFC shows greater activity than in non-volatile conditions (Training & Stable). Vertical axes in the left panel represent the equivalent learning rate of a reinforcement learning model (see Supplementary Note). Vertical axes in the center panel indicate a Bayesian estimate of volatility (left axis, gray bars) and model activity! in arbitrary units (right axis, black bars). This has been interpreted with a Bayesian model in which mPFC signals the expected volatility (right panel; bars indicate human behavior, circles indicate behavior of a Bayesian model, and the vertical axis represents the equivalent learning rate of a reinforcement learning model; adapted with permission from ref. 16). In the PRO model, greater volatility in a block led to greater mean ωN (center). Surprise signals, in turn, dynamically modulated the effective learning rate of the model (left), yielding lower effective learning rates (see Supplementary Note) during periods of greater stability (F1,3 = 70.3, P = 4.0 × 10−15). In the mPFC-lesioned model, learning rates did not significantly change between periods (F1,3 = 0.23, P = 0.88). () mPFC signals discrepancies between actual and expected outcomes. If errors occur more frequently than correct trials (in this case, 70% error rate), mPFC is predicted to show an inversion o! f the error effect—that is, greater activity (aligned on fee! dback) for correct than for error trials. () Delayed feedback effect. Feedback that is delayed an extra 400 ms on a minority of trials (20% here) leads to timing discrepancies and greater surprise activation (aligned on feedback). () Effects of reward salience on error prediction and detection. As rewarding events influence learning to a greater degree, error likelihood effects (aligned on trial onset) decrease while error effects (aligned on feedback) increase. The error and error likelihood effects are calculated as contrasts (as in ) and given in arbitrary units. All error bars indicate s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA. * William H Alexander & * Joshua W Brown Contributions J.W.B. and W.H.A. conceptualized the model. W.H.A. implemented the model and ran the simulations. J.W.B. and W.H.A. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Joshua W Brown Author Details * William H Alexander Search for this author in: * NPG journals * PubMed * Google Scholar * Joshua W Brown Contact Joshua W Brown Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (238K) Supplementary Figure 1, Supplementary Note and Supplementary Discussion Additional data - Neuronal activity modifies the DNA methylation landscape in the adult brain
- Nat Neurosci 14(10):1345-1351 (2011)
Nature Neuroscience | Resource Neuronal activity modifies the DNA methylation landscape in the adult brain * Junjie U Guo1, 2 * Dengke K Ma1, 3 * Huan Mo4 * Madeleine P Ball5 * Mi-Hyeon Jang1, 3 * Michael A Bonaguidi1, 3 * Jacob A Balazer6 * Hugh L Eaves4 * Bin Xie7 * Eric Ford8 * Kun Zhang9 * Guo-li Ming1, 2, 3 * Yuan Gao1, 7 * Hongjun Song1, 2, 3 * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:1345–1351Year published:(2011)DOI:doi:10.1038/nn.2900Received31 May 2011Accepted08 July 2011Published online28 August 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 DNA methylation has been traditionally viewed as a highly stable epigenetic mark in postmitotic cells. However, postnatal brains appear to show stimulus-induced methylation changes, at least in a few identified CpG dinucleotides. How extensively the neuronal DNA methylome is regulated by neuronal activity is unknown. Using a next-generation sequencing–based method for genome-wide analysis at single-nucleotide resolution, we quantitatively compared the CpG methylation landscape of adult mouse dentate granule neurons in vivo before and after synchronous neuronal activation. About 1.4% of 219,991 CpGs measured showed rapid active demethylation or de novo methylation. Some modifications remained stable for at least 24 h. These activity-modified CpGs showed a broad genomic distribution with significant enrichment in low-CpG density regions, and were associated with brain-specific genes related to neuronal plasticity. Our study implicates modification of the neuronal DNA methylo! me as a previously underappreciated mechanism for activity-dependent epigenetic regulation in the adult nervous system. View full text Figures at a glance * Figure 1: Modification of DNA methylation landscape in the adult dentate gyrus by neuronal activity. () Comparison of CpG methylation profiles of the dentate gyrus of adult mice at different time points after a single ECS. Shown are histograms of differential CpG methylation between sham control and 4 h after ECS (ΔMSCC,E4-E0; top) and between sham control and 24 h after ECS (ΔMSCC,E24-E0; bottom). Red lines represent Gaussian distributions with the same means and variances. Dashed lines represent ±20% cutoff. () A scatter plot of differential CpG methylation (E4-E0) at individual CpGs versus their basal methylation levels at E0 (MSCC estimates with 30 or more reads (MSCC30+); gray dots). Black dots indicate bisulfite analysis of selected MSCC sites for validation (filled, methylation change as measured by bisulfite analysis (ΔBisSeq) ≥20%; open, ΔBisSeq <20%). Below are heat maps of methylation changes detected by MSCC, bisulfite sequencing (seq) and HpaII-qPCR, from independent biological samples. * Figure 2: Persistence of activity-induced CpG modifications. () Histograms showing distributions of activity-modified CpGs (ΔMSCC,E4-E0 ≥20%) at E4 (top) and E24 (bottom). De novo methylated (black bars) and demethylated (gray bars) CpGs remain well segregated from E0 at E24. () Unsupervised hierarchical clustering of methylation levels of the top 500 MSCC sites with activity-induced modifications. The E24 profile is more similar to the E4 than to the E0, suggesting that activity-induced CpG modifications in mature neurons in vivo are fairly sustained. () Venn diagram showing highly significant overlapping between activity-modified CpGs identified at E4 and E24. Note that fewer MSCC sites (N) are used in the analysis due to the additional requirement of sequencing depth of the E24 sample (P value, exact binomial test). * Figure 3: Biological properties of activity-induced CpG methylation changes in the adult dentate gyrus. () Examples of bisulfite sequencing analysis of CpGs in five representative regions: Per2 (period homolog 2; demethylated in TSS upstream region; chr1:93356934–93357200), Crebbp (CREB binding protein; demethylated in the exon; chr16:4085747–4086068), Grip1 (glutamate receptor interacting protein 1; demethylated in the intron; chr10:119374673–119374888), Zfhx2 (zinc finger homeobox 2; de novo methylated in the exon; chr14:55691325–55691565) and Ccdc33 (coiled-coil domain–containing 33; de novo methylated in the intron; chr9:57897719–57898139). Each data point represents bisulfite sequencing results from an individual mouse for these representative regions of interest at E0 (gray circles) and E4 (open circles). Lines represent mean values. Arrowheads point to MSCC sites of interest. () Summaries of methylation changes at the MSCC sites upon different manipulations (with independent corresponding controls). CCP or saline was injected 1 h before ECS. RG108, 5-azacyti! dine (5-AzaC) or saline was infused into the lateral ventricles 2 d before ECS. Adult male Gadd45b knockout (G45b-KO) and wild-type (Control) littermates were used, as indicated. Data represent a minimum of 20 bisulfite reads for each condition from at least two mice. * Figure 4: Voluntary exercise–induced CpG methylation changes in dentate granule cells of the adult mouse hippocampus. () Strip plot of methylation of CpGs from three control mice (filled circles) and three mice allowed to run (open circles). Adult mice were housed in standard cages with or without free access to a running wheel for 3 d. Dentate gyrus tissues were microdissected for quantification of DNA methylation with HpaII-qPCR analysis for the same set of 48 CpGs as in Supplementary Figure 6a. () Heat maps for mean methylation changes of the set of 48 CpGs induced by a single ECS (at 4 h) or running (after 3 d). Values represent means from three sets of mice. About 67% of CpGs examined showed similar methylation changes (arrowheads) 4 h after ECS and after 3 d of running. * Figure 5: Genomic characteristics of activity-modified CpGs. () Enrichment of activity-induced methylation changes in regions with low CpG density. Top: distributions of CpG densities of 500-bp windows flanking activity-modified CpGs, all MSCC sites with ≥30 reads (MSCC30+ sites) and all CpGs in the mouse genome; Bottom: box plots showing median and quartiles of the three distributions (CGob, observed CpG number; CGex, expected CpG number; P value, Student's t-test). () Distribution of modified CpGs in different genomic subregions. Charts show unchanged (top) and activity-modified CpGs (bottom) mapped to each genomic subregion. TSS upstream, within 5 kb upstream from the TSS; TES downstream, within 5 kb downstream from the TES; intergenic, >5 kb away from any known gene. * Figure 6: Correlation between changes in CpG methylation and gene expression, and enrichment of activity-modified CpGs in brain-specific genes and neuronal pathways. () Correlation between activity-induced methylation changes of CpGs in different genomic subregions and mRNA level changes of associated genes between E4 and E0 (P values, Pearson's correlation test). (,) Tissue-specific expression () and gene ontology (GO) analysis () of genes associated with activity-modified CpGs. Only nonredundant GO terms are shown in . To control for the gene length effect, only GO terms that show P values less than 0.1 (Fisher's exact test) using each of the three independent random CpG sets generated from all MSCC sites with sequencing depth ≥30 are shown. Accession codes * Abstract * Accession codes * Author information * Supplementary information Referenced accessions Gene Expression Omnibus * GSE30493 Author information * Abstract * Accession codes * Author information * Supplementary information Affiliations * Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Junjie U Guo, * Dengke K Ma, * Mi-Hyeon Jang, * Michael A Bonaguidi, * Guo-li Ming, * Yuan Gao & * Hongjun Song * The Solomon Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Junjie U Guo, * Guo-li Ming & * Hongjun Song * Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Dengke K Ma, * Mi-Hyeon Jang, * Michael A Bonaguidi, * Guo-li Ming & * Hongjun Song * Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA. * Huan Mo & * Hugh L Eaves * Department of Genetics, Harvard Medical School, Cambridge, Massachusetts, USA. * Madeleine P Ball * Proofpoint Inc., Sunnyvale, California, USA. * Jacob A Balazer * Division of Genomics, Epigenomics and Bioinformatics, Lieber Institute for Brain Development, Baltimore, Maryland, USA. * Bin Xie & * Yuan Gao * Department of Radiation Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. * Eric Ford * Department of Bioengineering, University of California at San Diego, La Jolla, California, USA. * Kun Zhang Contributions J.U.G., D.K.M., G.M., Y.G. and H.S. designed the project. J.U.G. led and was involved in all aspect of the project. H.M. performed sequence mapping and methylation calculation. M.P.B. constructed libraries and performed initial methylation analysis. M.-H.J. assisted in BrdU analysis, irradiation and drug infusion procedures. M.A.B. performed FACS purification. J.A.B. wrote the gene mapping program. H.L.E. adapted MOM for the current project. B.X. and H.M. performed Illumina sequencing. E.F. contributed to irradiation studies. K.Z. contributed to data processing. J.U.G., G.M., Y.G. and H.S. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Hongjun Song or * Yuan Gao Author Details * Junjie U Guo Search for this author in: * NPG journals * PubMed * Google Scholar * Dengke K Ma Search for this author in: * NPG journals * PubMed * Google Scholar * Huan Mo Search for this author in: * NPG journals * PubMed * Google Scholar * Madeleine P Ball Search for this author in: * NPG journals * PubMed * Google Scholar * Mi-Hyeon Jang Search for this author in: * NPG journals * PubMed * Google Scholar * Michael A Bonaguidi Search for this author in: * NPG journals * PubMed * Google Scholar * Jacob A Balazer Search for this author in: * NPG journals * PubMed * Google Scholar * Hugh L Eaves Search for this author in: * NPG journals * PubMed * Google Scholar * Bin Xie Search for this author in: * NPG journals * PubMed * Google Scholar * Eric Ford Search for this author in: * NPG journals * PubMed * Google Scholar * Kun Zhang Search for this author in: * NPG journals * PubMed * Google Scholar * Guo-li Ming Search for this author in: * NPG journals * PubMed * Google Scholar * Yuan Gao Contact Yuan Gao Search for this author in: * NPG journals * PubMed * Google Scholar * Hongjun Song Contact Hongjun Song Search for this author in: * NPG journals * PubMed * Google Scholar Supplementary information * Abstract * Accession codes * Author information * Supplementary information Excel files * Supplementary Table 2 (37K) Primer sets used for bisulfite sequencing analysis of selected CpGs * Supplementary Table 3 (37K) Primer sets used for HpaII-qPCR analysis of selected CpGs * Supplementary Table 4 (11M) MSCC results for all MSCC30+ sites * Supplementary Table 5 (66K) List of repetitive sequences examined for CpG methylation changes * Supplementary Table 6 (4M) Mouse exon array expression profiles of dentate granule cells at E0 and E4 * Supplementary Table 7 (696K) Expression profiles of genes associated with activity-modified CpGs * Supplementary Table 8 (37K) Primer sets and results of q-PCR analysis of promoter CpG changes-associated genes * Supplementary Table 9 (9M) Functional pathways that contain activity-modified CpGs PDF files * Supplementary Text and Figures (3M) Supplementary Figures 1–22 and Supplementary Table 1 Additional data
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