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Neurons and Neuronal Activity Control Gene Expression in Astrocytes to Regulate Their Development and Metabolism

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Neurons and Neuronal Activity Control Gene Expression in Astrocytes to Regulate Their Development and Metabolism ARTICLE Received 29 Jul 2016 | Accepted 2 Mar 2017 | Published 2 May 2017 DOI: 10.1038/ncomms15132 OPEN Neurons and neuronal activity control gene expression in astrocytes to regulate their development and metabolism Philip Hasel1, Owen Dando1,2,3,*, Zoeb Jiwaji1,2,*, Paul Baxter1, Alison C. Todd1, Samuel Heron4,No´ra M. Ma´rkus1, Jamie McQueen1, David W. Hampton2, Megan Torvell2, Sachin S. Tiwari2, Sean McKay1, Abel Eraso-Pichot5, Antonio Zorzano6,7,8, Roser Masgrau5, Elena Galea5,9, Siddharthan Chandran2,3, David J.A. Wyllie1, T. Ian Simpson4 & Giles E. Hardingham1,10 The influence that neurons exert on astrocytic function is poorly understood. To investigate this, we first developed a system combining cortical neurons and astrocytes from closely related species, followed by RNA-seq and in silico species separation. This approach uncovers a wide programme of neuron-induced astrocytic gene expression, involving Notch signalling, which drives and maintains astrocytic maturity and neurotransmitter uptake function, is conserved in human development, and is disrupted by neurodegeneration. Separately, hundreds of astrocytic genes are acutely regulated by synaptic activity via mechanisms involving cAMP/PKA-dependent CREB activation. This includes the coordinated activity- dependent upregulation of major astrocytic components of the astrocyte–neuron lactate shuttle, leading to a CREB-dependent increase in astrocytic glucose metabolism and elevated lactate export. Moreover, the groups of astrocytic genes induced by neurons or neuronal activity both show age-dependent decline in humans. Thus, neurons and neuronal activity regulate the astrocytic transcriptome with the potential to shape astrocyte–neuron metabolic cooperation. 1 Deanery of Biomedical Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh EH8 9XD, UK. 2 MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh EH16 4SB, UK. 3 Centre for Brain Development and Repair, Institute for Stem Cell Biology and Regenerative Medicine, National Centre for Biological Sciences, Bangalore 560065, India. 4 School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK. 5 Institut de Neurocie`ncies and Departament de Bioquı´mica i Biologia Molecular, Unitat de Bioquı´mica de Medicina, Edifici M, Universitat Auto`noma de Barcelona, Bellaterra, Barcelona 08193, Spain. 6 Institute for Research in Biomedicine, Barcelona 08028, Spain. 7 Department of Biochemistry and Molecular Biology, University of Barcelona, Barcelona 08028, Spain. 8 Centro de Investigacio´n Biome´dica en Red de Diabetes y Enfermedades Metabo´licas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Madrid 28029, Spain. 9 Institucio´ Catalana De Recerca I Estudis Avanc¸ats (ICREA), Passeig Lluı´s Companys 23, Barcelona, Catalonia, 08010, Spain. 10 UK Dementia Research Institute at The University of Edinburgh, Edinburgh Medical School, 47 Little France Crescent, Edinburgh EH16 4TJ, UK. * These authors contributed equally to this work. Correspondence and requests for materials should be addressed to G.E.H. (email: [email protected]). NATURE COMMUNICATIONS | 8:15132 | DOI: 10.1038/ncomms15132 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15132 ignalling between neurons triggers programmes of gene another marker (Aldh1l1, 99.4±0.2% positive (n ¼ 4), Suppleme- expression that mediate specific functions in development ntary Fig. 2a). The astrocytes were o0.1% Neuro-Chrom þ Sand maturity. Signalling between neurons and glia is also neurons and o0.1% Iba þ microglia. implicated in diverse processes, however, insight into the scope Compared to parallel astrocytic mono-cultures, co-cultured and mechanism by which different cell types in the neuro-glial astrocytes developed the expected stellate morphology over 9 days unit influence each other is lacking. A notable example is neuron- (Fig. 1a,b). At this point, RNA was extracted (3 biological to-astrocyte signalling: neurons induce the classic stellate replicates), RNA-seq performed and SSS employed to identify the morphology in astrocytes, resembling their appearance in vivo1. mouse (that is, astrocyte) reads. DGE analysis revealed wide- However, knowledge of the transcriptional changes that spread up- and down-regulation of astrocytic gene expression due accompany this transformation are restricted to three to the presence of neurons (Fig. 1c; Supplementary Data 5), astrocytic genes (Slc1a2, Slc1a3 and Gja1 (refs 2,3)) induced by including previously identified genes (Slc1a2, Slc1a3 and Gja1 neurons. Similarly, despite astrocytes playing a critical role in the (refs 2,3)). Four hundred and ten genes were induced more than uptake of synaptically released neurotransmitters, whether twofold and 353 genes repressed more than twofold (Adjusted P synaptic activity controls astrocytic gene expression is unclear. value o0.05, Supplementary Data 5). This contrasts with the wealth of knowledge concerning the A notable cluster of upregulated genes is involved in the mechanisms and the roles of activity-dependent gene expression uptake and metabolism of neurotransmitters, including in neurons4–7. GABA (Slc6a1, Slc6a11 and Abat), NAAG (Slc17a5), biogenic Understanding non-cell-autonomously regulated gene expres- amine neurotransmitters (Slc29a2 and Maob) and glutamate sion poses challenges associated with the physical separation of (Slc1a2, Slc1a3, Glul and Glud1; Supplementary Data 6). cell types before transcriptome analysis by methods including Focussing on glutamate uptake, we confirmed that these RNA-seq. Immunopanning or FACS are powerful approaches, transcriptional changes are reflected at the functional level, by but are subject to off-target cell type contamination, and cause measuring the electrogenic currents of the glutamate transporters aberrant induction of transcriptional stress responses, or the loss EAAT1/GLAST (Slc1a3), and EAAT2/GLT-1 (Slc1a2), using of material from delicate subcellular regions8–10. Theoretically, if whole-cell voltage clamp recordings. Astrocytic EAAT currents14 RNA-seq reads could be unambiguously attributed to one cell were increased around 10-fold by neuronal co-culture (Fig. 1d,e), type or another, physical sorting could be avoided. We while astrocytic resting membrane potential and passive memb- investigated the possibility of achieving this by deriving the rane conductance were unaffected (Supplementary Fig. 2b,c)15,16. distinct cell types from different species11. We chose rat and In addition, many cytoskeletal and extracellular matrix genes mouse, two species that we reasoned may be closely enough were altered (Supplementary Data 5), consistent with the related to enable questions of non-cell-autonomous signalling to profound changes in astrocytic morphology. Astrocyte-specific be answered. We used this system as a starting point to marker and endfoot component Aqp4 was also induced, but other investigate how and to what extent neurons and neuronal markers, S100b, Aldh1l1 and Gfap were unaffected (Fig. 1c). Thus, activity control astrocytic gene expression, as well as the neuron-induced changes to the morphology of astrocytes are underlying mechanisms and functional consequences. associated with marked changes to their transcriptome and functional properties. Results To assess the advantages of this approach over imperfect physical separation approaches, we firstly simulated a sorting Unsorting rat and mouse RNA-seq reads in silico. We first established the feasibility of our species-specific sorting (SSS) process that achieved 95% purity, by ‘contaminating’ the mouse astrocytic mRNA with mouse cortical neuronal mRNA at a ratio RNA-seq workflow. In the vast majority of genes, the majority of mouse RNA-seq paired-end reads can be unambiguously attributed of 95:5, by number of cells collected, and performed RNA-seq. The effects were substantial: 863 genes were expressed more than to mouse (as opposed to rat), using both a simulated set of reads (Supplementary Fig. 1a; Supplementary Data 1) as well as two-fold higher in the 95% sample, compared to the pure astrocytic sample (Supplementary Fig. 3a, Supplementary Data 7), a ‘real-world’ RNA-seq data set of mouse cortical neurons 4 (Supplementary Fig. 1b; Supplementary Data 2). Also, compared to of which 216 were expressed 10-fold higher. Second, we investigated the induction of transcriptional stress responses to normal read mapping, the SSS workflow does not substantially 9 affect differential gene expression analysis (DGE) when performed physical sorting methods . Both simple trypsinization, immunopanning and FACS protocols applied to a on a single-species (mouse) RNA-seq data-set taken from neurons treated±bicuculline þ 4-aminopyridine (BiC/4-AP) to induce homogeneous population of cultured astrocytes induced multiple immediate early genes (Supplementary Fig. 3b–d). synaptic activity via network disinhibition12:weobservedatight correlation in fold-induction, comparing normal and SSS These data show key advantages of in silico read separation in avoiding DGE artefacts introduced by physical sorting. approaches (Supplementary Fig. 1c; Supplementary Data 3). There was also little change in the adjusted P values: of the 4,632 genes altered by BiC/4-AP treatment, only 33 were rendered Neurons induce an in vivo-like transcriptome in astrocytes insignificant by the SSS approach (Supplementary Fig. 1d; . While neuronal co-culture promotes a more in vivo-like Supplementary Data 4). Large P value deviations were more
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