Karen E Carney

Andrea Goudriaan

Nutabi Camargo

Stéphane H R Oliet

August B Smit

Mark H G Verheijen

Marco Milanese

Pim Van Nierop

Ka Wan Li

Giambattista Bonanno

Roel C Van Der Schors

Priyanka Rao-Ruiz

Nikhil J Pandya

Rolinka J Van Der Loo

Sabine Spijker

Novel cell separation method for molecular analysis of neuron-astrocyte cocultures

Keywords: astrocytes, protéomiques, plasticité synaptique Proteomic Characterization of Perisynaptic Astrocytes in Synaptic Plasticity astrocytes, proteomics, synaptic plasticity Neurocentre Magendie

Caractérisation Protéomique Des

Astrocytes: a myriad of functions

Astrocytes are the most numerous cell type in the human brain, comprising more than 1/3 of its total mass [START_REF] Kandel | Nerve cell and behavior[END_REF]. They form a heterogeneous population that varies in morphology and biochemical properties [START_REF] Oberheim | Heterogeneity of astrocytic form and function[END_REF], and even neighboring astrocytes can exhibit different protein expression profiles and morphologies [START_REF] Matthias | Segregated expression of AMPA-type glutamate receptors and glutamate transporters defines distinct astrocyte populations in the mouse hippocampus[END_REF][START_REF] Bonfanti | Radial glia-like cells in the supraoptic nucleus of the adult rat[END_REF]. This diversity of astrocyte subtypes was first fully appreciated in the elaborate illustrations of Ramón y Cajal using the staining technique of Camillo Golgi [START_REF] Ramon Y Cajal | Contribucion al conocimiento de la neuroglia del cerebro humano[END_REF]. Currently, two primary classes of astrocytes are recognized: fibrous astrocytes found in the white matter with few processes, and protoplasmic astrocytes residing in the grey matter exhibiting elaborate branching [START_REF] Oberheim | Heterogeneity of astrocytic form and function[END_REF]. Highly specialized astrocytes appear in the retina and cerebellum, known as Muller glia and Bergmann glia respectively, and express some astrocyte marker proteins but exhibit distinct morphologies and functions [START_REF] Oberheim | Heterogeneity of astrocytic form and function[END_REF][START_REF] Kettenmann | Neuroglia: the 150 years after[END_REF]. As the emphasis of this thesis is the role of astrocytes at the synapse, I will focus on the characteristics of the grey matter protoplasmic astrocytes.

Protoplasmic astrocytes are composed of 5 to 8 primary glial fibrillary acidic protein (GFAP)positive processes that ramify into numerous smaller peripheral processes [START_REF] Bushong | Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains[END_REF]. They are arranged in a tiled fashion such that there is very little overlap between the approximate 66,000 μm 3 territories of neighboring cells [START_REF] Bushong | Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains[END_REF]. Fine astrocytic processes extend, for instance, to ensheath synapses whereas larger diameter processes envelop blood vessels [START_REF] Derouiche | Beyond polarity: functional membrane domains in astrocytes and Muller cells[END_REF]. Each type of process is enriched for proteins that mediate their primary functions, indicating that each astrocyte is comprised of functional microdomains [START_REF] Derouiche | Beyond polarity: functional membrane domains in astrocytes and Muller cells[END_REF][START_REF] Nagelhus | Aquaporin-4 in the central nervous system: cellular and subcellular distribution and coexpression with KIR4.1[END_REF][START_REF] Forsythe | Synaptic transmission: well-placed modulators[END_REF][START_REF] Reichenbach | Morphology and dynamics of perisynaptic glia[END_REF].

In accordance with the diversity of their morphology and protein contents, astrocytes are attributed with a range of biological functions. Traditionally, astrocytes have been primarily acknowledged for their roles as supportive cells providing metabolic support to neurons. Astrocytic endfeet, rich in molecular transporters, wrap around blood vessels, contributing to the formation of the blood-brain barrier and control the brain's supply of nutrients [START_REF] Nico | Morphofunctional aspects of the blood-brain barrier[END_REF]. In a process known as functional hyperemia, astrocyte processes at the synapse detect changes in neuronal firing rates, likely via increased K + levels, and relay this signal to the endfeet contacting the vasculature [START_REF] Dunn | Potassium channels and neurovascular coupling[END_REF]. Modulatory signals are then released onto the blood vessels to adjust the vasculature diameter, and hence blood flow, enhancing delivery of glucose and oxygen to brain regions with increased synaptic activity [START_REF] Iadecola | Glial regulation of the cerebral microvasculature[END_REF].

Contact with the synapse allows astrocytes to uptake excess neurotransmitters and ions, such as glutamate and K + , thus maintaining an optimal environment for neuronal function [START_REF] Simard | The neurobiology of glia in the context of water and ion homeostasis[END_REF][START_REF] Kreft | Aspects of astrocyte energy metabolism, amino acid neurotransmitter homoeostasis and metabolic compartmentation[END_REF]. Uptake of glutamate is important for the neuron-astrocyte lactate shuttle, in which astrocytes convert glutamate into lactate that can be released back to neurons to fuel their metabolic demands during synaptic activity [START_REF] Magistretti | Neuron-glia metabolic coupling and plasticity[END_REF]. As neurons are unable to fully synthesize glutamate on their own, they also rely on astrocytes for participation in the glutamate metabolic cycle.

Glutamate taken up by astrocytes is converted to glutamine and then shuttled back to neurons where they subsequently transforms it back to glutamate for use in synaptic transmission [START_REF] Hertz | Astrocytic control of glutamatergic activity: astrocytes as stars of the show[END_REF]. K + buffering by astrocytes transfers K + from sites of high concentration, such as active synapses, to those with lower concentrations in order to maintain neuronal membrane potentials and prevent excitotoxicity and epileptiform activity [START_REF] Kofuji | Potassium buffering in the central nervous system[END_REF][START_REF] Steinhauser | Astrocyte dysfunction in temporal lobe epilepsy: K+ channels and gap junction coupling[END_REF]. Although these are considered "supportive" astrocyte functions they are nonetheless critical to normal brain function.

Increasingly complex regulatory mechanisms of astrocytes at the synapse are becoming more clearly delineated. Astrocytes are credited with enhancing synaptogenesis, regulating neurogenesis, and inducing synaptic pruning, so are thus critical for the proper development of neuronal connections [START_REF] Ullian | Control of synapse number by glia[END_REF][START_REF] Allen | Astrocyte glypicans 4 and 6 promote formation of excitatory synapses via GluA1 AMPA receptors[END_REF][START_REF] Song | Astroglia induce neurogenesis from adult neural stem cells[END_REF][START_REF] Clarke | Emerging roles of astrocytes in neural circuit development[END_REF]. Several instances of bidirectional communication have been observed between neurons and astrocytes. Astrocytes detect signals released from neurons and respond with the secretion of neuromodulatory factors that alter synaptic function and neuronal network activity. This has led to the concept that many synapses are in fact tripartite synapses, containing neuronal pre-and post-synaptic structures along with the nearby astrocytic process [START_REF] Araque | Gliotransmitters travel in time and space[END_REF][START_REF] Araque | Tripartite synapses: glia, the unacknowledged partner[END_REF][START_REF] Santello | Gliotransmission and the tripartite synapse[END_REF]. The accumulating evidence that astrocytes are not just passive responders to neuronal activity but can directly control neuronal development and synaptic function has served to fuel burgeoning interest in the field of neuron-glia interactions.

The tripartite synapse: astrocytic means for controlling neuronal activity

Origin of the tripartite synapse concept

Glial cells have long been the enigmatic cells of the brain. Since they do not show electrical activity, unlike neurons, the properties and functions of glia remained elusive to the physiology pioneers of early neuroscience. Most were of the opinion that astrocytes were merely passive supportive cells comprising the glue that held the brain together [START_REF] Kettenmann | Neuroglia: the 150 years after[END_REF]. However, in the late 19 th century Ramón y Cajal was one of the first to remark that astrocytes may be involved in the modulation of synaptic function by serving as a physical barrier to insulate the activity of neighboring neurons [START_REF] Navarrete | The Cajal school and the physiological role of astrocytes: a way of thinking[END_REF]. It would take another century of scientific research for the accumulating evidence of the roles of astrocytes in synaptic function to be formally recognized with the coining of the term "tripartite synapse" [START_REF] Araque | Tripartite synapses: glia, the unacknowledged partner[END_REF].

From the 1970s-1990s, increasing interest in the role of glial cells and ever evolving experimental techniques led to important discoveries about astrocytic properties. Electron microscopy revealed that astrocyte plasma membranes directly contact synapses, and the extent of this contact can be altered by various physiological stimuli [START_REF] Chapman | Osmotic stimulation causes structural plasticity of neurone-glia relationships of the oxytocin but not vasopressin secreting neurones in the hypothalamic supraoptic nucleus[END_REF][START_REF] Theodosis | Structural plasticity in the hypothalamic supraoptic nucleus at lactation affects oxytocin-, but not vasopressin-secreting neurones[END_REF][START_REF] Wenzel | The influence of long-term potentiation on the spatial relationship between astrocyte processes and potentiated synapses in the dentate gyrus neuropil of rat brain[END_REF][START_REF] Adams | Synaptic remodelling and astrocytic hypertrophy in rat cerebral cortex from early to late adulthood[END_REF]. Astrocytes and neurons are metabolically coupled, with synaptic glutamate release inducing astrocytic production of lactate to meet neuronal energy demands [START_REF] Tsacopoulos | Metabolic coupling between glia and neurons[END_REF][START_REF] Pellerin | Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism coupling neuronal activity to glucose utilization[END_REF]. It was also demonstrated that astrocytes express receptors and transporters for a vast range of neurotransmitters, allowing them to sense extracellular levels of neurotransmitters and to release neurotransmitters, technically termed gliotransmitters when of glial origin, into the synaptic cleft [START_REF] Santello | Gliotransmission and the tripartite synapse[END_REF][START_REF] Verkhratsky | Neurotransmitter Receptors in Astrocytes, in Astrocytes in (Patho)Physiology of the Nervous System[END_REF][START_REF] Mennerick | Glial contributions to excitatory neurotransmission in cultured hippocampal cells[END_REF]. A pivotal moment in the path leading to the tripartite synapse concept was the discovery that astrocytes are able to respond to synaptic neurotransmitters with intracellular Ca 2+ waves [START_REF] Cornell-Bell | Glutamate induces calcium waves in cultured astrocytes: long-range glial signaling[END_REF]. Although these Ca 2+ signals occurred on a slower time-scale than the electrical signals of neuronal action potentials, they illuminated a novel widespread signaling network within the brain running parallel to that of the neuronal network. As calcium is known to function as a second messenger to regulate a multitude of cellular programs, there was now a probable mechanism to explain how astrocytes could process and respond to signals received from the synaptic environment [START_REF] Heizmann | Calcium signaling in the brain[END_REF][START_REF] Cornell-Bell | Ca2+ waves in astrocytes[END_REF]. Cumulatively, these observations supported the view that synapses are comprised not only of the neuronal preand post-synaptic compartments, but also the fine perisynaptic astrocyte process ensheathing the synapse (Figure 1). This close proximity allows for bidirectional communication between neurons and astrocytes thus forming a functional unit known as the tripartite synapse [START_REF] Araque | Tripartite synapses: glia, the unacknowledged partner[END_REF]. Means of modulation of the synaptic environment by astrocytes at the tripartite synapse include the secretion or clearance of neuromodulatory substances, morphological rearrangements that alter diffusion properties of the synaptic space, and heterosynaptic actions that can affect network activity. The underlying mechanisms and examples of the tripartite synapse will now be discussed. Figure 1. A hippocampal excitatory tripartite synapse depicting some of the known pathways for neuron-astrocyte interactions. A) Neurotransmitters such as glutamate (Glu) and ATP are released from the pre-synaptic terminal (blue). These can bind metabotropic receptors on the perisynaptic astrocyte (green) and result in the IP3-dependent release of Ca 2+ from endoplasmic reticulum (ER) stores. The Ca 2+ increases can initiate the release of gliotransmitters (Glu, D-serine, ATP) into the extracellular space or into the synaptic cleft where they can bind neuronal receptors. Presynaptic receptor activation can either enhance (Glu) or decrease (ATP) neurotransmitter release probability. Post-synaptic receptor (red) activation can enhance NMDA-receptor currents (D-serine) or modulate AMPA-receptor expression (TNFα). Synaptic glutamate can also be taken up by astrocytic glutamate transporters (EAATs) to regulate glutamate availability. The initiated Ca 2+ wave can propagate to neighboring astrocytes via gap junctions (GJC) or via extracelluar ATP-dependent signaling to transmit the wave to distant astrocytes. Reprinted with permission from reference [START_REF] De Pitta | Computational quest for understanding the role of astrocyte signaling in synaptic transmission and plasticity[END_REF].

The tripartite synapse mechanisms of action: Calcium signaling and gliotransmission

Calcium signaling

Astrocytes are known to respond to synaptic stimulation with oscillations of their internal Ca 2+ levels [START_REF] Scemes | Astrocyte calcium waves: what they are and what they do[END_REF]. This discovery was initially made using glutamate application on cultured astrocytes [START_REF] Cornell-Bell | Glutamate induces calcium waves in cultured astrocytes: long-range glial signaling[END_REF], but has been repeated in both acute slices [START_REF] Porter | Hippocampal astrocytes in situ respond to glutamate released from synaptic terminals[END_REF] and in vivo [START_REF] Wang | Astrocytic Ca2+ signaling evoked by sensory stimulation in vivo[END_REF] and is mediated by activation of metabotropic glutamate receptors. These Ca 2+ oscillations also occur in response to a variety of stimuli, including activation of other neurotransmitter receptors (including, GABA [START_REF] Serrano | GABAergic network activation of glial cells underlies hippocampal heterosynaptic depression[END_REF][START_REF] Kang | Astrocyte-mediated potentiation of inhibitory synaptic transmission[END_REF], AMPA [START_REF] Piet | Glutamatergic and purinergic receptor-mediated calcium transients in Bergmann glial cells[END_REF], norepinephrine [START_REF] Duffy | Adrenergic calcium signaling in astrocyte networks within the hippocampal slice[END_REF][START_REF] Nilsson | Adrenergic and 5-HT2 receptors on the same astroglial cell. A microspectrofluorimetric study on cytosolic Ca2+ responses in single cells in primary culture[END_REF], serotonin [START_REF] Nilsson | Adrenergic and 5-HT2 receptors on the same astroglial cell. A microspectrofluorimetric study on cytosolic Ca2+ responses in single cells in primary culture[END_REF], endocannabinoids [START_REF] Navarrete | Endocannabinoids potentiate synaptic transmission through stimulation of astrocytes[END_REF], ATP [START_REF] Bowser | Vesicular ATP is the predominant cause of intercellular calcium waves in astrocytes[END_REF], histamine [START_REF] Shelton | Hippocampal astrocytes exhibit Ca2+-elevating muscarinic cholinergic and histaminergic receptors in situ[END_REF], and acetylcholine [START_REF] Shelton | Hippocampal astrocytes exhibit Ca2+-elevating muscarinic cholinergic and histaminergic receptors in situ[END_REF][START_REF] Navarrete | Astrocytes mediate in vivo cholinergic-induced synaptic plasticity[END_REF]), signaling molecules like TNFα [START_REF] Santello | TNFalpha controls glutamatergic gliotransmission in the hippocampal dentate gyrus[END_REF] and nitric oxide [START_REF] Matyash | Nitric oxide signals parallel fiber activity to Bergmann glial cells in the mouse cerebellar slice[END_REF], membrane depolarization [START_REF] Macvicar | Modulation of intracellular Ca++ in cultured astrocytes by influx through voltage-activated Ca++ channels[END_REF], and mechanical stimulation [START_REF] Bourque | Central mechanisms of osmosensation and systemic osmoregulation[END_REF]. This rise in internal Ca 2+ is an intricately encoded signal involving both spatial and temporal components so that oscillations induced by different stimuli encode different information regarding neural activity and thus lead to different responses [START_REF] Araque | Gliotransmitters travel in time and space[END_REF]. Although large Ca 2+ increases can be measured in the astrocyte soma, neurotransmitter receptors situated on astrocytic plasma membranes near the synapse can mediate small, rapid, localized Ca 2+ oscillations in response to synaptic activity [START_REF] Panatier | Astrocytes are endogenous regulators of basal transmission at central synapses[END_REF][START_REF] Castro | Local Ca2+ detection and modulation of synaptic release by astrocytes[END_REF]. Activity of several of these microdomains may be summated to form slower soma-level calcium oscillations that ultimately lead to global changes in synapse function, although definitive proof of this summation is lacking [START_REF] Araque | Gliotransmitters travel in time and space[END_REF].

Ca 2+ oscillations are not restricted to the stimulated astrocyte, but rather propagate through gap junctions to hundreds or thousands of adjacent astrocytes, forming a large astrocyte syncytium [START_REF] Konietzko | Astrocytic dye coupling in rat hippocampus: topography, developmental onset, and modulation by protein kinase C[END_REF]. Furthermore, distinct sub-networks of gap-junction coupling can be identified within a single region, serving to confine information within a distinct physiological circuit [START_REF] Houades | Gap junction-mediated astrocytic networks in the mouse barrel cortex[END_REF][START_REF] Wallraff | Distinct types of astroglial cells in the hippocampus differ in gap junction coupling[END_REF]. While astrocytes cultured alone do show spontaneous Ca 2+ oscillations in single cells, intercellular Ca 2+ waves only occur in the presence of neurons [START_REF] Charles | Glia-neuron intercellular calcium signaling[END_REF]. This connection of intracellular milieu allows propagation of the wave of Ca 2+ through the astrocyte network, transmitting information from one synapse across the astrocyte network to distant synapses, underlying astrocytic modulation of heterosynaptic plasticity [START_REF] Cornell-Bell | Glutamate induces calcium waves in cultured astrocytes: long-range glial signaling[END_REF][START_REF] Hulme | Mechanisms of heterosynaptic metaplasticity[END_REF]. Although distant propagation of Ca 2+ waves is frequently observed in experimental culture conditions, the extent to which the signal can propagate in vivo is less clear and may be considerably more restricted [START_REF] Sul | Astrocytic connectivity in the hippocampus[END_REF].

This increase in Ca 2+ can be mediated via multiple pathways. A well-studied mechanism is via release from internal Ca 2+ stores. Gq-protein coupled neurotransmitter receptors are expressed on astrocyte plasma membranes [START_REF] Porter | Astrocytic neurotransmitter receptors in situ and in vivo[END_REF]. Stimulation of these receptors by neuronal activity activates phospholipase C and the subsequent formation of inositol [START_REF] Kandel | Nerve cell and behavior[END_REF][START_REF] Bonfanti | Radial glia-like cells in the supraoptic nucleus of the adult rat[END_REF][START_REF] Ramon Y Cajal | Contribucion al conocimiento de la neuroglia del cerebro humano[END_REF]triphosphate (IP3). IP3 then binds primarily to the IP3 receptor type 2 (IP3R2) located on the endoplasmic reticulum (ER) in astrocytes, thus releasing stored Ca 2+ [START_REF] Petravicz | Loss of IP3 receptor-dependent Ca2+ increases in hippocampal astrocytes does not affect baseline CA1 pyramidal neuron synaptic activity[END_REF][START_REF] Agulhon | What is the role of astrocyte calcium in neurophysiology?[END_REF]. In a process known as Ca 2+ -induced Ca 2+ release (CICR), intracellular Ca 2+ rises can themselves active ER IP3 receptors, as well as ER ryanodine receptors, stimulating additional release of Ca 2+ and thus enhancing the Ca 2+ signal [START_REF] Parpura | Ca(2+) sources for the exocytotic release of glutamate from astrocytes[END_REF]. Additionally, ATP and glutamate released from astrocytes are capable of autocrine and paracrine signaling between astrocytes via extracellular routes that also facilitate the propagation of Ca 2+ waves based on release from internal stores [START_REF] Bezzi | A neuron-glia signalling network in the active brain[END_REF][START_REF] Hassinger | An extracellular signaling component in propagation of astrocytic calcium waves[END_REF].

Other less well-studied means of intracellular Ca 2+ increases have been reported to rely on influx of extracellular Ca 2+ . Reversal of Na + / Ca 2+ exchangers, which are enriched in perisynaptic astrocyte processes [START_REF] Minelli | Cellular and subcellular localization of Na+-Ca2+ exchanger protein isoforms, NCX1, NCX2, and NCX3 in cerebral cortex and hippocampus of adult rat[END_REF], have been shown to mediate Ca 2+ influxes in response to mild depolarization [START_REF] Paluzzi | Adult astroglia is competent for Na+/Ca2+ exchanger-operated exocytotic glutamate release triggered by mild depolarization[END_REF]. Astrocytes also express transient receptor potential channels (TRPCs), a type of store-operated calcium channel, that play a role in mechanically-evoked Ca 2+ increases [START_REF] Malarkey | Ca2+ entry through TRPC1 channels contributes to intracellular Ca2+ dynamics and consequent glutamate release from rat astrocytes[END_REF]. Influx of Ca 2+ can also be a consequence of activation of voltage-gated Ca 2+ channels, although this pathway is generally associated with spontaneous astrocyte Ca 2+ oscillation rather than astrocyte Ca 2 waves induced by nearby neuronal activity [START_REF] Agulhon | What is the role of astrocyte calcium in neurophysiology?[END_REF][START_REF] Carmignoto | On the role of voltage-dependent calcium channels in calcium signaling of astrocytes in situ[END_REF]. The increase of intracellular Ca 2+ , be it derived from intracellular or extracellular stores, has important roles for tripartite synapse function [START_REF] Parpura | Ca(2+) sources for the exocytotic release of glutamate from astrocytes[END_REF].

Gliotransmission

But how does a Ca 2+ wave in astrocytes lead to the modulation of synaptic transmission? The most well-studied mechanism is through the Ca 2+ -triggered release of gliotransmitters.

Gliotransmitters are substances released by astrocytes within tens of milliseconds to seconds after stimulation and bind to neuronal receptors leading to altered neuronal function for a period of seconds to minutes [START_REF] Araque | Gliotransmitters travel in time and space[END_REF][START_REF] Santello | Gliotransmission and the tripartite synapse[END_REF]. They include a wide range of factors including glutamate [START_REF] Parpura | Ca(2+) sources for the exocytotic release of glutamate from astrocytes[END_REF], D-serine [START_REF] Henneberger | Long-term potentiation depends on release of D-serine from astrocytes[END_REF], GABA [START_REF] Jo | GABA from reactive astrocytes impairs memory in mouse models of Alzheimer's disease[END_REF][START_REF] Le Meur | GABA release by hippocampal astrocytes[END_REF], ATP [START_REF] Gordon | Norepinephrine triggers release of glial ATP to increase postsynaptic efficacy[END_REF] , TNFα [START_REF] Stellwagen | Synaptic scaling mediated by glial TNF-alpha[END_REF], and taurine [START_REF] Choe | Taurine release by astrocytes modulates osmosensitive glycine receptor tone and excitability in the adult supraoptic nucleus[END_REF]. The release of these factors is often mediated by G-protein coupled receptors on the astrocyte plasma membrane that are activated upon neuronal activity, including those for glutamate, GABA, acetylcholine, and purines [START_REF] Verkhratsky | Neurotransmitter Receptors in Astrocytes, in Astrocytes in (Patho)Physiology of the Nervous System[END_REF]. Activation of the receptors initiates a Ca 2+ wave, which subsequently serves as the internal signaling molecule to commence gliotransmitter release mechanisms [START_REF] Santello | Gliotransmission and the tripartite synapse[END_REF].

The most clearly understood gliotransmitter-release mechanism is regulated vesicular exocytosis. Secretory vesicles with gliotransmitter cargo can fuse to the membrane upon Ca 2+ increases in a soluble N-ethyl maleimide-sensitive fusion protein attachment protein receptor (SNARE)-complex dependent fashion [START_REF] Bezzi | Astrocytes contain a vesicular compartment that is competent for regulated exocytosis of glutamate[END_REF][START_REF] Crippa | Synaptobrevin2-expressing vesicles in rat astrocytes: insights into molecular characterization, dynamics and exocytosis[END_REF][START_REF] Montana | Vesicular glutamate transporter-dependent glutamate release from astrocytes[END_REF]. This mechanism has received abundant attention as it is considered the most likely to be relevant under physiological conditions as it allows for precisely timed and located gliotransmitter release [START_REF] Santello | Gliotransmission and the tripartite synapse[END_REF]. Non-Ca 2+ dependent mechanisms for gliotransmitter release have also been reported, but are generally associated with pathological conditions [START_REF] Santello | Gliotransmission and the tripartite synapse[END_REF]. These include reversal of glutamate transporters [START_REF] Szatkowski | Non-vesicular release of glutamate from glial cells by reversed electrogenic glutamate uptake[END_REF], swelling-evoked release [START_REF] Kimelberg | Swelling-induced release of glutamate, aspartate, and taurine from astrocyte cultures[END_REF], P2X7 receptor channels [START_REF] Duan | P2X7 receptor-mediated release of excitatory amino acids from astrocytes[END_REF], cysteine-glutamate antiporter [START_REF] Bender | Characterization of cystine uptake in cultured astrocytes[END_REF], and release via membrane hemichannels [START_REF] Orellana | Hemichannels: new roles in astroglial function[END_REF]. However, not all calcium increases induce gliotransmitter release and neuronal modulation [START_REF] Fiacco | Selective stimulation of astrocyte calcium in situ does not affect neuronal excitatory synaptic activity[END_REF][START_REF] Agulhon | Hippocampal short-and long-term plasticity are not modulated by astrocyte Ca2+ signaling[END_REF]. In addition, normal neuronal activity has been observed even when astrocytic calcium signaling has been disabled by deletion of astrocytic IP3 receptor type 2 [START_REF] Petravicz | Loss of IP3 receptor-dependent Ca2+ increases in hippocampal astrocytes does not affect baseline CA1 pyramidal neuron synaptic activity[END_REF][START_REF] Agulhon | Hippocampal short-and long-term plasticity are not modulated by astrocyte Ca2+ signaling[END_REF]. This observation has led to debate over the importance of the tripartite synapse [START_REF] Hamilton | Do astrocytes really exocytose neurotransmitters?[END_REF]. Seemingly similar experiments in the same brain region have led to contradictory results, suggesting our existing models of gliotransmission are too simplistic and require further refinement [START_REF] Volterra | Astrocyte Ca(2)(+) signalling: an unexpected complexity[END_REF].

From what has been observed, the effects of gliotransmission depend largely on which gliotransmitters are released and at which location. The same gliotransmitter can act on multiple targets and illicit differential neuronal responses [START_REF] Araque | Gliotransmitters travel in time and space[END_REF]. Equally important is the preexisting spatial environment at the tripartite synapse that can dictate which neuronal receptors are exposed to gliotransmitters [START_REF] Piet | Physiological contribution of the astrocytic environment of neurons to intersynaptic crosstalk[END_REF][START_REF] Sykova | Extrasynaptic volume transmission and diffusion parameters of the extracellular space[END_REF][START_REF] Bernardinelli | Astrocyte-synapse structural plasticity[END_REF]. We will next turn to specific examples of astrocytic modulation of synaptic activity in the two brain regions focused on in this thesis, the supra optic nucleus and hippocampus, as well as examples from other brain regions.

Manifestations of the tripartite synapse a. In the hippocampus

Gliotransmitters in the hippocampus exhibit diverse and complex modulatory functions, with the same gliotransmitter exerting different functions or acting simultaneously on pre-and post-synaptic targets [START_REF] Araque | Gliotransmitters travel in time and space[END_REF]. For instance, astrocytic release of ATP, which is rapidly converted to adenosine by ectonucleotidases, can regulate heterosynaptic depression [START_REF] Serrano | GABAergic network activation of glial cells underlies hippocampal heterosynaptic depression[END_REF][START_REF] Zhang | ATP released by astrocytes mediates glutamatergic activity-dependent heterosynaptic suppression[END_REF][START_REF] Pascual | Astrocytic purinergic signaling coordinates synaptic networks[END_REF] and basal neurotransmission [START_REF] Panatier | Astrocytes are endogenous regulators of basal transmission at central synapses[END_REF][START_REF] Castro | Local Ca2+ detection and modulation of synaptic release by astrocytes[END_REF] by acting on pre-synaptic receptors, whereas effects on LTP [START_REF] Pascual | Astrocytic purinergic signaling coordinates synaptic networks[END_REF][START_REF] Deng | Astrocytic activation of A1 receptors regulates the surface expression of NMDA receptors through a Src kinase dependent pathway[END_REF] are the result of alterations in post-synaptic NMDA-receptor expression.

Furthermore, pre-synaptic effects of ATP can either facilitate or depress neurotransmission depending on whether they bind adenosine A1 or A2A receptors [START_REF] Panatier | Astrocytes are endogenous regulators of basal transmission at central synapses[END_REF][START_REF] Pascual | Astrocytic purinergic signaling coordinates synaptic networks[END_REF]. Similarly, the gliotransmitter glutamate has been shown to exert neuromodulatory effects by acting on presynaptic NMDA receptors in the dentate gyrus [START_REF] Jourdain | Glutamate exocytosis from astrocytes controls synaptic strength[END_REF] whereas in the CA1 presynaptic mGluRs [START_REF] Navarrete | Endocannabinoids potentiate synaptic transmission through stimulation of astrocytes[END_REF], presynaptic kainate receptors [100], and post synaptic NMDA receptors [101] are targets for glutamate. Additional neuromodulation possibilities by the release of D-serine [START_REF] Henneberger | Long-term potentiation depends on release of D-serine from astrocytes[END_REF] (as discussed in further detail below), TNFα [102], and GABA [START_REF] Le Meur | GABA release by hippocampal astrocytes[END_REF] in the hippocampus in response to neuronal activity add further layers of complexity of how astrocytes exert concerted effects at the tripartite synapse.

Astrocytes in the hippocampus are also able to integrate signals from stimulation of different synaptic pathways and can react with a localized response. For example, in the CA1, glutamatergic input from the Schaffer collaterals and cholinergic inputs originating from the alveus invoke an astrocyte Ca 2+ response that is not merely the sum of the Ca 2+ responses initiated by glutamate or acetylcholine alone [103]. This Ca 2+ response was either enhanced or depressed based on the intensity of neuronal stimulation, and responses could be seen at the level of single processes. Ca 2+ increases evoked NMDA receptor-mediated slow inward currents, likely mediated by astrocytic glutamate release, providing evidence for discrete astrocytic microdomains with complex government over the integration of synaptic information [103]. Functional astrocytic microdomains are also important for the modulation of basal transmission in the CA1. Even after a single action potential, mGluR5-mediated, IP3Rdependent, rapid Ca 2+ increases are observed in discrete functional compartments of astrocytic processes. These Ca 2+ signals result in vesicular-release of purines from astrocytes that bind presynaptic adenosine A2A receptors and up-regulate basal synaptic transmission [START_REF] Panatier | Astrocytes are endogenous regulators of basal transmission at central synapses[END_REF][START_REF] Castro | Local Ca2+ detection and modulation of synaptic release by astrocytes[END_REF]. Thus, astrocytes do not exhibit simply reflexive responses to neuronal activity but rather can actively integrate inputs from multiple regions and transform these into neuromodulatory signals with high spatial precision as a result of functional microdomains located along their processes [104].

b. In the supraoptic nucleus

Function-dependent neuron-glia plasticity in the supraoptic nucleus has been studied for decades. Oxytocin and vasopressin magnocellular neurons in this nucleus release hormones into the blood stream to control parturition and the milk ejection reflex (oxytocin) or regulate water balance (primarily vasopressin). The SON undergoes rapid morphological changes in response to physiological stimulation, including dehydration, hyperosmosis, and lactation, where SON production of the hormones oxytocin and vasopressin are increased [105].

Stimulation of the nucleus results in retraction of astrocyte processes from synapses onto oxytocin magnocellular neurons, while coverage of neighboring vasopressin magnocellular neurons remains the same [START_REF] Chapman | Osmotic stimulation causes structural plasticity of neurone-glia relationships of the oxytocin but not vasopressin secreting neurones in the hypothalamic supraoptic nucleus[END_REF][START_REF] Theodosis | Structural plasticity in the hypothalamic supraoptic nucleus at lactation affects oxytocin-, but not vasopressin-secreting neurones[END_REF]. The specificity of glial plasticity to the oxytocin neurons indicates that this phenomenon mediates specific biological processes at the SON tripartite synapse.

Retraction of astrocyte processes modulates synaptic function via several mechanisms.

Removal of the physical barrier between neighboring synapses alters the diffusion properties of the extracellular space. In the stimulated SON, glutamate spillover is enhanced, facilitating intersynaptic crosstalk and heterosynaptic modulation [START_REF] Piet | Physiological contribution of the astrocytic environment of neurons to intersynaptic crosstalk[END_REF]. Glutamate is also allowed to diffuse more easily as glutamate transporters present on the retracted perisynaptic astrocyte processes (PAPs) are less efficient at glutamate reuptake [106]. Higher levels of glutamate in the stimulated SON act on presynaptic group III metabotropic glutamate receptors (mGluRs) to decrease neuronal firing rate, but can also diffuse through the extracellular space, bind mGluRs on neighboring GABAergic terminals, and decrease inhibitory inputs [START_REF] Piet | Physiological contribution of the astrocytic environment of neurons to intersynaptic crosstalk[END_REF]106]. In addition to reduced glutamate reuptake by retracted PAPs, availability of gliotransmitters is also decreased in the SON. Astrocytes in the SON have been shown to be the sole providers of the NMDA-receptor co-agonist D-serine and thus control the activity of synaptic NMDA receptors and consequently synaptic potentiation [107]. Thus, these astrocytic modifications to diffusion properties of the extracellular space, decreased re-uptake of glutamate, and decreased availability of D-serine converge to alter synaptic function in the stimulated SON in a fashion that promotes neuronal synchronicity while restricting potentiation only to the most active synapses. However, the consequences of these alterations in synaptic function as a result of glial plasticity on behavior of stimulated animals remains unclear [108].

c. In other brain regions

In the cortex, synchronized depolarization of groups of neurons leads to sustained burst firing, known as UP states [109]. This type of synchrony is seen during the processing of stimuli and is thought to contribute to the integration of information across distant brain regions [110,111]. Electrical stimulation of a single astrocyte results in UP state synchronization of nearby neurons, which is dependent on astrocytic Ca 2+ signaling and regulation of extracellular glutamate [112]. Similar to the hippocampus and SON, astrocyte-derived D-serine can also act as an endogenous NMDA receptor co-agonist in cortical regions [113,114]. D-serine release from astrocytes in layers V/VI of the medial prefrontal cortex is required for normal LTP induction [113], and release in the barrel cortex is involved in mediation of somatosensory plasticity in response to whisker stimulation [114].

In the cerebellum, Bergmann glia processes ensheath Purkinje cell (PC) spines almost completely and expresses glutamate transporters to uptake excess neurotransmitters and maintain synaptic fidelity [115,116]. These processes express GluA2-lacking AMPA receptors, making them highly Ca 2+ permeable. However, if these AMPA receptors are altered, the glial processes retract from the synapses allowing the formation of additional synapses onto the PCs and impairing glutamate clearance [117]. Glial process retraction alters PC currents and impairs performance of a fine motor coordination task, possibly as a result of impaired timing of PC firing [118]. During development, Bergmann-glia derived D-serine activates ionotropic delta δ-2 type glutamate receptors (GluD2) on PCs. In contrast to the synaptic potentiation effects of D-serine by activation of post-synaptic NMDA receptors as previously discussed [START_REF] Henneberger | Long-term potentiation depends on release of D-serine from astrocytes[END_REF]107], D-serine binding to GluD2 in the cerebellum induces AMPA receptor endocytosis and enhances LTD induction. These effects are mediated by a PKC-dependent pathways relying on the C-terminal domain of GluD2, but are only present in immature animals, again illustrating the complexity and diversity of neuronal responses to a single gliotransmitter [119].

The tripartite synapse also exists in developing brains. Studies using cultures of retinal ganglion cells (RGCs) demonstrate that astrocytes have roles in the formation and maturation of synapes [START_REF] Clarke | Emerging roles of astrocytes in neural circuit development[END_REF]. RGCs cultured in the presence of astrocyte-derived factors develop significantly more functionally mature synapses [START_REF] Ullian | Control of synapse number by glia[END_REF]120]. Thrombospondins secreted by immature astrocytes enhance the structural formation of synapses, although these are postsynaptically silent [121]. However, astrocyte secreted glypican-4 facilitates insertion of GluA1containing AMPA receptors into the post-synaptic density inducing the formation of functional synapses [START_REF] Allen | Astrocyte glypicans 4 and 6 promote formation of excitatory synapses via GluA1 AMPA receptors[END_REF]. The astrocytic matricelluar protein hevin increased the size and number of synapses formed between RGCs, while astrocyte-derived secreted protein acidic and rich in cysteine (SPARC) antagonizes the action of hevin, providing a molecular mechanism whereby astrocytes can specifically regulate the timing of synapse formation [122]. As both hevin and SPARC are expressed in the adult brain, this modulatory mechanism may persist in vivo and might be used to control synaptic plasticity.

Perisynaptic astrocyte processes

PAPs are highly motile and ensheath synapses across the brain

Because of their close association to the synapse, astrocytic processes contacting synaptic elements have been termed perisynaptic astrocyte processes (PAPs) [START_REF] Bernardinelli | Astrocyte-synapse structural plasticity[END_REF]. A single rodent astrocyte can contact more than 100,000 synapses, making them ideally poised to integrate and relay information between distant synapses [START_REF] Bushong | Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains[END_REF]. The extent of synaptic coverage by astrocytic processes varies between brain regions and by stimulation level or physiological condition [START_REF] Chapman | Osmotic stimulation causes structural plasticity of neurone-glia relationships of the oxytocin but not vasopressin secreting neurones in the hypothalamic supraoptic nucleus[END_REF][START_REF] Theodosis | Structural plasticity in the hypothalamic supraoptic nucleus at lactation affects oxytocin-, but not vasopressin-secreting neurones[END_REF][START_REF] Wenzel | The influence of long-term potentiation on the spatial relationship between astrocyte processes and potentiated synapses in the dentate gyrus neuropil of rat brain[END_REF]. Even within the same structure large variations in the amount of glial coverage can occur. For example, in the cerebellum, 15% of mossy fiber terminals are contacted by astrocyte processes while an estimated 67% of parallel fiber synapses and 94% of climbing fiber synapses are ensheathed [123,124], indicating that different degrees of ensheathment impart physiologically relevant synaptic regulations.

Approximately 50% of hippocampal synapses are contacted by PAPs at any given time [125].

However, astrocyte processes generally do not fully encapsulate synapses, thus allowing for neurotransmitter spillover and synaptic cross-talk [125]. Astrocytic processes in the hippocampus are extremely motile, repeatedly extending and retracting from the synapse on the time-scale of minutes [126,127]. Astrocyte contacts are the most stable at large dendritic spines, suggesting their presence may be required for on-going modulation of more active synapses [126]. PAPs preferentially associate with the post-synaptic element which is thought to allow presynaptic-feedback of synaptically released glutamate [128]. After synaptic potentiation, glial coverage of both the pre-and post-synaptic terminals increases, particularly so at large synapses, indicating that activity-induced structural plasticity of PAPs at hippocampal synapses is important to synaptic plasticity [START_REF] Wenzel | The influence of long-term potentiation on the spatial relationship between astrocyte processes and potentiated synapses in the dentate gyrus neuropil of rat brain[END_REF]129]. Accordingly, morphological rearrangements that reduce glial invasion of synapses and glutamate uptake impair synaptic plasticity and cause contextual fear memory deficits [130].

In addition to the previously discussed architectural alterations seen in the SON and hippocampus, PAP motility is also seen in other brain regions. In the suprachiasmatic nucleus, astrocyte processes rhythmically extend and retract from synapses over the 24-hour lightdark cycle, which may facilitate entrainment of the circadian clock [131]. Rearing animals in a complex environment, as opposed to a standard laboratory cage, induces increased astrocyte coverage of synapses in the visual cortex [132]. In the barrel cortex, 24 hours of whisker stimulation results in increased astrocyte coverage of synapses, as well as a 2-fold increase in expression of the astrocytic glutamate transporters . These results indicate astrocyte morphological adaptations are important mediators of experience-driven plasticity.

Differences in the number of synapses contacted by astrocytes and their degree of ensheathment can alter synaptic function by multiple pathways. Variations in the extent of astrocytic coverage can affect the availability of gliotransmitters or act as a diffusion barrier regulating the extent of neurotransmitter spill-over between synapses and thus modulating synaptic connectivity [START_REF] Piet | Physiological contribution of the astrocytic environment of neurons to intersynaptic crosstalk[END_REF][START_REF] Sykova | Extrasynaptic volume transmission and diffusion parameters of the extracellular space[END_REF]125]. Increased astrocytic coverage of active synapses may also serve to clear the synaptic cleft of excess neurotransmitters and ions while simultaneously enhancing delivery of metabolic substrates to support neuronal function [START_REF] Bernardinelli | Astrocyte-synapse structural plasticity[END_REF]. Direct astrocyte contact with dendritic spines promotes spine maturation and stability in the developing brain by activation of the protein kinase C pathway, and could also be important for stabilization of newly formed synapses in the adult brain during learning and memory [134,135]. The overall large variablitiy of synaptic ensheathment across the brain is an indication that astrocytes are highly sensitive to neuronal activity and alter their morphology accordingly to promote specific synaptic functions.

Contents of PAPs

Although PAPs are less than 1 µm in diameter and not more than 10 µm long, they constitute 70-80% of the astrocytic plasma membrane [START_REF] Bushong | Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains[END_REF][START_REF] Bernardinelli | Astrocyte-synapse structural plasticity[END_REF]127]. PAPs are smaller than the diffraction limit imposed by light microscopy, rendering them difficult to evaluate with convention imaging techniques [126]. Electron microscopy (EM) provides the best resolution for physical neuron-astrocyte interactions, although it does not allow for evaluation of dynamic changes in real-time. EM has revealed that the fine nature of PAPs precludes the presences of many large organelle, including mitochondria and endoplasmic reticulum, however the inclusion of ribosomes suggests local protein translation is important to PAP function [START_REF] Bernardinelli | Astrocyte-synapse structural plasticity[END_REF].

The proteins present in PAPs facilitate their functions at the tripartite synapse. Large microtubules, such as GFAP, are not expressed in PAPs [START_REF] Bushong | Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains[END_REF]. However, actin binding proteins such as ezrin, radixin, and profilin-1 are present, which contribute to PAP motile capacities [136][137][138]. The glutamate transporters GLT-1 and GLAST are enriched in PAPs to facilitate synaptic clearance of excessive glutamate and regulate synaptic efficacy [START_REF] Bushong | Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains[END_REF]106,130]. GLAST is anchored to the PAP membrane by the predominantly astrocytic cytoskeletal adaptor protein Na + /H + exchanger regulatory cofactor 1 (NHERF1), which likely localizes GLAST to the PAP via ezrin binding [139]. While also present on perivascular endfeet, perisynaptically localized aquaporin-4 regulates the volume of the extracellular space and has complex roles in the modulation of synaptic potentiation and subsequent behavior [140,141]. The potassium channel (Kir) 4.1 and Na + /K + -transporting ATPase subunit alpha-2 regulate the ionic balance of the synaptic environment by buffering excess K + and have both been shown to play important roles in synaptic function and memory consolidation [142][143][144]. Because the proteins known to be present in PAPs reflect their functional roles at the synapse, identification of additional PAP proteins is a useful means toward the identification of novel astrocytic contributions to synaptic function.

Aims and outline of the thesis

As the tripartite synapse plays critical roles in the modulation of synaptic function and subsequently behavior, further research into the underlying mechanisms of these processes are warranted. While Ca 2+ imaging is the primarily tool employed to assess astrocytic responses to neuronal stimulation, its usefulness has limitations. The small diameter of PAPs allows only soma-level monitoring of Ca 2+ changes and the artificial nature of Ca 2+ uncaging or stimulation of transgenically-expressed exogenous receptors do not accurately model the precisely timed and localized Ca 2+ wave induced by physiological stimuli [START_REF] Agulhon | What is the role of astrocyte calcium in neurophysiology?[END_REF]. We therefore sought to explore alternative methods for the evaluation of astrocyte-derived factors modulating synaptic activity and PAP-level protein changes in response to synaptic plasticity that may have functional roles at the tripartite synapse.

In this thesis I explore the utility of several preparations for evaluation of tripartite synapse function (Chapters 2 and 3) and then utilizes these techniques to identify candidate genes and proteins regulated by models of synaptic plasticity . In Chapter 2 I established an in-vitro model of neuron-glia interactions using astrocyte-neuron co-cultures.

Development of the Cold-jet technique enabled preferential removal of neurons from astrocytes in mature co-cultures, allowing for the isolation of two distinct cell populations that can be used for the identification of genes regulated specifically by neuron-astrocyte interactions. In Chapter 3 I turn my focus specifically to PAP proteins. Mass spectrometry was used to compare the PAP content in three biochemically-isolated fractions enriched for synaptic components to identify the most useful preparation for evaluation of expression level changes in PAP proteins.

To discern specific astrocyte proteins involved in plasticity, I next screened astrocyte protein content using two different models of functional and synaptic plasticity. Chapter 4 details proteome changes in the SON as a result of hyperosmosis and lactation-induced structural and functional plasticity. In Chapter 5, astrocyte protein regulations as the result of contextual fear memory consolidation were determined. Selected proteins from Chapters 4 and 5 identified to be involved in plasticity were chosen for the experiments of Chapter 6, a proteinlevel expression profiling over a time-course of contextual fear memory consolidation. Levels of 15 astrocyte proteins were evaluated over the course of consolidation between 4 and 72 hours and the effects of one of the regulated proteins, MLC1, was evaluated in paradigms of learning and memory using MLC1 null mice.

ABSTRACT

Over the last decade, the importance of astrocyte-neuron communication in neuronal development and synaptic plasticity has become increasingly clear. Since neuron-astrocyte interactions represent highly dynamic and reciprocal processes, we hypothesized that many astrocyte genes may be regulated as a consequence of their interactions with maturing neurons. In order to identify such neuron-responsive astrocyte genes in vitro, we sought to establish an expedited technique for separation of neurons from co-cultured astrocytes. Our newly established method makes use of cold jet, which exploits different adhesion characteristics of subpopulations of cells [145], and is rapid, performed under ice-cold conditions and avoids protease-mediated isolation of astrocytes or time-consuming centrifugation, yielding intact astrocyte mRNA with approximately 90% of neuronal RNA removed. Using this purification method, we executed genome-wide profiling in which RNA derived from astrocyte-only cultures was compared with astrocyte RNA derived from differentiating neuron-astrocyte co-cultures. Data analysis determined that many astrocytic mRNAs and biological processes are regulated by neuronal interaction. Our results validate the cold jet as an efficient method to separate astrocytes from neurons in co-culture, and reveals that neurons induce robust gene-expression changes in co-cultured astrocytes.

INTRODUCTION

During central nervous system (CNS) development, glia and neurons are targeted to their proper location in the brain and the appropriate contacts between neurons, as well as neurons and glia cells, are established. While neuroscience research historically has focused on synaptic contacts between neurons, the last couple of decades it has become apparent that reciprocal neuron-glia interactions are crucial for correct brain functioning. The functionally diverse glia-neuron interactions rely on a range of both contact-dependent mechanisms as well as soluble factors [146,147]. The development of elaborate in vitro systems, using highly purified cell populations, has strongly contributed to the elucidation of the molecular effects of glia on neuronal development and function [START_REF] Ullian | Control of synapse number by glia[END_REF]148,149].

Specifically, the use of astrocyte-neuron co-cultures has led to the identification of several astrocyte-derived signals that induce neuritogenesis and synaptogenesis, at least in vitro. For instance, lipids are secreted by astrocytes in high quantities and are well known to promote neurite outgrowth and synapse formation, [150][151][152][153][154]. Other astrocyte-secreted factors that were shown to influence synapse development are extracellular matrix molecules, such as thrombospondins and glypicans [START_REF] Clarke | Emerging roles of astrocytes in neural circuit development[END_REF].

Whereas new astrocyte factors affecting neuronal function are likely to be discovered, a prominent deficit in our knowledge is how expression and secretion of these astrocyte factors are controlled. Neuronal synaptic activity has been shown to induce transcriptional activity in astrocytes [155]. It thus follows that synaptic activity and neuron-glia communication will likely elicit gene expression changes in glia, which in turn might reciprocally affect the process of synapse maturation and synaptic transmission. In order to identify neuron-responsive astrocyte genes, we established a developmental in vitro transcriptional profiling experiment using neuron-astrocyte co-cultures. To specifically isolate astrocytes from the co-culture, we successfully developed a fast method, named 'cold jet' that exploits different adhesion characteristics of subpopulations of cells [145]. The method lacks disadvantages of other methods, e.g. that of FACS or magnetic bead-based cell sorting involving protease-mediated dissociation of cells [156,157], which cannot be efficiently conducted at low temperatures, thereby compromising both protein and mRNA quality. Using cold jet-mediated isolation of astrocytes, followed by microarray analysis, we identified nearly 400 mRNAs significantly regulated in astrocytes when co-cultured with neurons. The resulting database confirms the validity of the used cold jet method for separation of astrocytes and neurons, and represents an important gene source towards understanding of neuron-glia communication.

METHODS

Cell culture

Neuron-astrocyte co-cultures were done using rat astrocytes, because of tissue culture advantages of rat over mouse astrocytes, and mouse neurons to make it possible for future experiments to use neurons of genetically modified mice. Importantly, in these co-cultures, rat astrocyte provide the required support for optimal synaptic functioning of co-cultured mouse neurons [START_REF] Wierda | Interdependence of PKC-dependent and PKC-independent pathways for presynaptic plasticity[END_REF], an important requirement for our screen of neuroninduced expression changes in astrocytes. Primary rat astrocytes were obtained from Wistar rat pups (P1). Briefly, dissected cortices were treated with trypsin (Gibco) followed by addition of trypsin inhibitor and gentle trituration, after which cells were resuspended in DMEM + Glutamax (Gibco) containing 10% fetal bovine serum and cultured in non-coated plastic flasks.

Medium was changed every 2 days. The purity of astrocyte cultures was determined by immunostaining for GFAP and S100β, while counterstained for all cell nuclei using Hoechst, and was determined to be higher than 98%. For neuron-astrocyte co-cultures, astrocytes were plated at a density of 30K/well in 12 wells plates in DMEM + 10% medium, which resulted in an about 80% confluent monolayer at DIV 3, after which neurons were plated. For co-plating of neurons, hippocampi of E18 mouse embryos were collected in Hanks Buffered Salts Solution (HBSS; Sigma), buffered with 7 mM HEPES. After removal of the meninges, hippocampi were minced and incubated for 20 min in 0.25% trypsin in HBSS at 37°C. After washing, the neurons were triturated with fire-polished Pasteur pipettes, counted, and plated in culture medium [Neurobasal medium (Invitrogen, Carlsbad, USA) supplemented with 2% B-27 (Invitrogen), 1.8% HEPES, 1% glutamax (Invitrogen), 1% Pen/Strep (Invitrogen), and 0.2% beta-mercaptoethanol] at a density of 60K/ well onto the astrocyte monolayers in 12 well plates (4 cm²; Greiner bio-one) [158]. Wells were coated with 20% D-poly-lysine (Sigma) and 20% collagen diluted in a 17 µM acetic acid H20 solution. Cultures were maintained in 5% CO2 at 37°C, and half of the medium was changed at 1 day and 7 days after plating. Control conditions (astrocytes alone) were kept under similar tissue culture conditions as neuronastrocyte co-cultures.

Cold jet separation method

For removal of neurons from the co-cultured astrocytes, we modified a method previously used for removal of Schwann cells from co-cultured fibroblasts [145]. 12-well tissue culture plates were placed on ice and the medium was gently aspirated. The cells were carefully rinsed once with ice-cold PBS (pH 7.4). Next, using a 1 mL pipette tip, 1mL of ice-cold PBS was pipetted onto the co-cultures using a concentric circular motion, ensuring the force of the stream contacted the entire well surface area, which was repeated 3 times. This resulted in specific detachment of neurons, which were aspirated, leaving purified astrocytes, which were harvested for molecular analysis. All together, the isolation of astrocytes from cocultures for molecular isolation per well was performed within a timeframe of 30 seconds.

When co-culture cold jet (CC-CJ) cultures were compared to astrocyte alone cultures, the later were also treated by cold jet, therefore called AA-CJ samples.

Immunocytochemistry

Astrocyte monolayers or astrocyte-neuron co-cultures, before or after cold jet, were fixed by 4% paraformaldehyde in 0.1 M PBS for 15 min at RT. Cells were incubated 30 min in 0.1 M PBS containing 0.5% TritonX100 and 10% normal goat serum, and subsequently incubated with appropriate primary antibody concentrations in incubating medium (0.1 M PBS, 0.1% Triton-100, 2% normal goat serum) at 4°C overnight. Cells were washed 3 times for 10 min in 0.1 M PBS and incubated in incubating medium containing the appropriated secondary antibody for 2 hours. Sections were washed 2 times with 0.1 M PBS, 1 time with water, and subsequently DNA counterstained in Hoechst reagent 33258 solution (Sigma). The primary antibodies were rabbit anti-GFAP (1:500; Dako) and chicken anti-MAP2 (1:5000; Abcam). Secondary antibodies were donkey anti-rabbit Cy3 (1:400; Jackson laboratories), goat antichick Alexa488 (1:400; Molecular probes).

RNA isolation, reverse transcription and quantitative PCR

Total RNA was extracted from 12-well plates and mRNA was isolated with Trizol Reagent (Invitrogen). RNA samples for microarray analysis were derived from 2 to 4 tissue culture wells, and microarray replicates were derived from different pools of animals, with 5 to 8 animals per pool. An additional chloroform extraction was performed to remove traces of phenol and samples were suspended in H2O. The quality and quantity of RNA samples for microarray analysis were assessed on Agilent RNA chips with the Agilent 2100 Bioanalyzer (Agilent Technologies). For quantitative PCR, 4 samples were used that were different than those used for microarray analysis, and each sample was derived from different animals and 4 to 6 tissue culture wells. RNA integrity was determined using electrophoresis with RNA loading dye on a 1% agarose gel, RNA quantity was determined by photospectrometry (ND-1000; Thermo Scientific). Quantitative PCR reactions were done as described [159].

Microarray analysis

Agilent whole rat genome 4x 44K chips were used. cDNA synthesis and labeling for microarray hybridization were performed according to the manufacturers' instructions. Briefly, oligo dT primers were used and Cy3 and Cy5 labeling was done using the Agilent two-color RNA Spike-In Kit. 0.5 µg of Cy5 and Cy3-labeled cRNA targets were hydrolyzed and mixed for 30 min.

Arrays were hybridized with Cy5 and Cy3 solution for 18h at 60°C in a rotating hybridization chamber, using 1x hybridization solution (Agilent technologies). Subsequently, arrays were washed, spun dry and scanned using an Agilent scanner.

Analysis of the microarrays served 2 purposes: 1) detection of neuron-induced gene expression in astrocytes and 2) determination of the cold jet efficiency on the mRNA level. In total, 4 arrays were used. For the first purpose, 2 co-culture cold jet (CC-CJ) samples were labeled with Cy3 and 2 astrocyte-alone cold jet (AA-CJ) samples were labeled with Cy5. Each CC-CJ sample was hybridized together with an AA-CJ sample to an array. For checking the cold jet procedure on the mRNA level, 2 co-culture (CC) samples were labeled with Cy5 fluorescence and hybridized on an array together with a CC-CJ sample (Cy3). The microarray dataset can be found at NCBI GEO (accession number GSE52481).

Normalization of the raw data was performed, after feature extraction by Agilent software, by Optimized Local Intensity-dependent Normalization (OLIN) and Optimized Scaled Local Intensity-dependent Normalization (OSLIN) [160] using the R project for statistical computing (version 6.0; r-project). Next, M values were scaled so that upregulated probes were associated with positive values and downregulated probes with negative values. The following criteria were used for selection of regulated transcripts: 1) for each array, the mean and standard deviation (SDEV) of the M values of all probes was calculated, and only mRNAs with M values equal or higher to the mean + 2x SDEV, or equal or lower to the mean -2x SDEV were selected, 2) M values had to fulfill our criteria on both replicate arrays, 3) for mRNAs that were represented on the microarray with multiple probes, data were checked for consistency, and probes were only included in the analysis when replicate sequences gave comparable results, 4) mRNAs upregulated in the CC-CJ with AA-CJ comparison could represent left-over neuronal RNA when also strongly downregulated in the CC with CC-CJ comparison. Therefore, mRNAs upregulated in the CC-CJ with AA-CJ comparison where only included when downregulation in the CC with CC-CJ comparison was less than the mean -2x SD. To assess consistency between technical and biological replicate samples, as well as between replicate probes, non-parametric correlations (using Spearman's rho) within nonnormalized red or green fluorescence samples were calculated. For technical/biological replicate samples, correlations were only performed on regulated probes as indicated by the cold jet-astrocyte alone arrays.

Thereafter, data were analyzed by TopGo [161] using the R project for statistical computing.

Overrepresented biological processes were identified by scoring statistical significance of predefined annotated groups based on the Gene Ontology (GO) database. Statistical significance of overrepresented predefined GO biological processes was scored using the Fisher exact test [161].

RESULTS

Effectiveness of the cold jet method on neuron-astrocyte co-cultures

Neuronal development in hippocampal mixed neuron-glial cultures is a temporal process involving neurite outgrowth (1 DIV onwards), and synapse formation from 4 DIV onwards [162]. Since attachment of neurons to the extracellular culture environment might vary with time, cold jet removal of neurons from neuron-astrocyte co-cultures was tested at 10 and 14 DIV. Quantification of the number of neuronal somas and neurites, indicated that cold jet removed over 98% of neuronal somas and 80% of neurites at 10 DIV, and was only slightly

Gene-expression analysis of neuron-responsive astrocyte genes

Microarray analysis of CC-CJ versus AA-CJ samples, yielded 394 neuron-induced regulations of astrocyte mRNAs, of which 270 were upregulated and 124 downregulated. The 36 strongest up-and down-regulated mRNAs are shown (Fig. 3). The group of 270 mRNAs included many which are involved in astrocyte-neuron interactions, such as cell adhesion (Itgb,Pcdh10,Cdh6,Ctnnal1,Glycam,L1cam and TSP precursor 4), extracellular matrix (Cspg5 and Reck) and cytokine signaling (Megf6 and Lcn2). Several mRNAs involved in lipid synthesis were found down-regulated, including mRNAs involved in cholesterol synthesis (Dhcr7,Hmgcs,Sc4mol,Cyp26b,Tm7sf2,FPS,Sql,and Insig1). To gain more insight into biological processes represented by the data set, we scored statistical significance of predefined functional genes groups, based on Gene Ontology (GO) biological processes. From the complete list of regulated mRNAs (394), 10 significant overrepresented GO terms were identified (with p<0.005, see Table 1). Overrepresented GO terms included mainly metabolic processes, e.g. sterol and nutrient related processes. GO analysis of only up-regulated (Table 2) or only down-regulated (Table 3) gene groups showed most strikingly that mRNAs associated with 'sterol metabolism' were mainly down-regulated, whereas 'response to nutrient' processes were up-regulated. To validate the microarray data, qPCR was performed on a selection of mRNAs that were regulated between astrocytes alone (AA-CJ) and astrocytes in co-culture (CC-CJ). qPCR confirmed the down-regulation of three mRNAs involved in lipid metabolism (Insig1, Hmgcs, and Dhcr7) and confirmed the up-regulation of the transcription factor Egr1 in astrocytes when co-cultured with neurons (Fig 4).

Figure 4. Quantitative PCR analysis of neuron-responsive astrocytes genes. Transcript levels were measured using qPCR for a selected group of astrocyte-expressed mRNAs that according to microarray were neuron-responsive (in MA exp 1 and MA exp 2). The ratio between expression in CC-CJ vs AA-CJ was calculated and plotted on a Log2 scale, e.g. expression of EGR1 was 3.7 fold higher in CC-CJ then in AA-CJ. Data represents the mean and SDEV of 4 samples.

DISCUSSION

Cold jet: method for gentle separation of neurons from co-cultured astrocytes

Neuron-glia co-cultures are an established in vitro model to investigate the reciprocal interactions of these cell types that underlie neurite outgrowth, synapse formation and synaptic transmission. Investigating the molecular changes in astrocytes that take place during interaction requires the efficient isolation of astrocytes from the co-culture in a manner that ensures cellular integrity. Here, we present cold jet as a fast and efficient isolation method of astrocytes from differentiating neuron-astrocyte co-cultures. We showed that at 10 and 14 DIV, when neurites and mature synapses have been formed [162], cold jet removed nearly all neuronal RNA in these co-cultures. This permits selective transcriptional analysis of only astrocytes in these co-cultures. Cold jet isolation of astrocytes from neuronastrocyte co-cultures may also be suitable for analysis of proteins and lipids, since more than 90% of neuronal cell bodies and nearly as much neurites are removed. We conclude that cold jet offers important advantages over other techniques that are used for cell separation, such as FACS or immuno-panning. Unlike those other techniques, cold jet is fast (within 1 minute), easy to perform, done without addition of proteases, and conducted under ice-cold conditions to ensure no detriment to molecular integrity.

Identification of neuron-responsive astrocyte genes

Through a developmental in vitro model, in which neurons in co-culture with astrocytes differentiate and form synapses, we set out to identify neuron-responsive astrocyte gene expression using microarray analysis. We found regulation of many genes involved in astrocyte biological processes that have been reported previously to affect neuronal differentiation and function, such as lipid metabolism [150,159,163 150,153,168,169]. However, we did find downregulation of Insig1, an important negative regulator of the lipogenic SREBP transcription factors, which therefore would be expected to lead to increased SREBP-mediated transcription of lipogenic enzymes, as found in other cell systems [170]. Our recent observation that SREBPs are also required for expression of lipogenic enzymes in astrocytes

[159], warrants further investigation of the control of Insig, SREBP, and downstream transcription in astrocytes due to contact with neurons. Taken together, our observations suggest that astrocyte-factors involved in these processes do not only influence neuronal differentiation but are furthermore induced in astrocytes by neuronal presence, underscoring the reciprocal nature of the neuron-glia interactions.

INTRODUCTION

Traditionally, astrocytes are associated with the accommodating roles of providing metabolic support to neurons, regulating blood flow, and clearing excessive neurotransmitters and ions from the synaptic cleft [START_REF] Tsacopoulos | Metabolic coupling between glia and neurons[END_REF][START_REF] Pellerin | Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism coupling neuronal activity to glucose utilization[END_REF]171,172]. Currently, astrocytes and neuronal synapses are additionally credited as being active bi-directional partners in the modulation of synaptic transmission [173]. Astrocytes are able to sense changes in the extracellular environment that are indicative of neuronal activity and subsequently respond with the release of neuromodulatory factors, termed gliotransmitters, which can act on both the pre-and postsynapse to regulate synaptic activity [START_REF] Santello | Gliotransmission and the tripartite synapse[END_REF]. This intimate, reciprocal relationship between neurons and astrocytes has been termed the tripartite synapse [START_REF] Araque | Tripartite synapses: glia, the unacknowledged partner[END_REF]. Astrocytes also integrate and relay information between non-synaptically linked neurons, using calcium-wave dependent and independent means, to tune neuronal network activity [START_REF] Serrano | GABAergic network activation of glial cells underlies hippocampal heterosynaptic depression[END_REF][START_REF] Navarrete | Endocannabinoids potentiate synaptic transmission through stimulation of astrocytes[END_REF][START_REF] Piet | Physiological contribution of the astrocytic environment of neurons to intersynaptic crosstalk[END_REF]173,174].

For instance, astrocytic glutamate transporters GLT-1 and GLAST are enriched in the glial leaflets facing active synapses [175] and the proximity of astrocytic processes to the synapse influences glutamate clearance efficiency, which consequently modulates synaptic transmission [106,130]. The cell membrane-cytoskeletal linker protein Ezrin has been specifically localized in the brain to astrocytes and is crucial to perisynaptic astrocyte process (PAP) motility seen during synaptic plasticity [137,138]. The multiple roles of perisynaptic astrocytes in regulating synaptic function [START_REF] Henneberger | Long-term potentiation depends on release of D-serine from astrocytes[END_REF][START_REF] Pascual | Astrocytic purinergic signaling coordinates synaptic networks[END_REF] and behavior [176,177] have often been found to involve vesicular release of gliotransmitters. Accordingly, astrocyte express SNARE complex proteins, for which there are astrocyte-specific isoforms, e.g. VAMP3 and Snap23

[178].

Whereas hundreds of neuronal proteins in pre-and post-synaptic membranes have been identified [179,180], and insight into their functioning of the synapse is increasing [181][182][183],

it is largely unknown which astrocyte proteins are involved in the functional interaction with neurons. Knowing the astrocyte proteins involved in neuron-astrocyte interactions will allow us to infer the biological processes undertaken by astrocytes at the synapse. We have previously reported that gliosomes, a subcellular fraction isolated using a Percoll ® gradient, seem to be enriched for astrocyte subcellular particles [184]. Compared to synaptosome preparations, gliosomes are enriched for astrocyte specific proteins including GFAP and S100, they contain astrocyte glutamate transporters as well as exocytosis machinery components, implying they contain the molecular machinery capable of gliotransmission [START_REF] Paluzzi | Adult astroglia is competent for Na+/Ca2+ exchanger-operated exocytotic glutamate release triggered by mild depolarization[END_REF]. Accordingly, gliosomes preparations have been employed to assess the release and uptake of various neurotransmitters by astrocytes under physiological and pathological conditions [185][186][187].

To establish a more detailed catalog of astrocyte proteins potentially involved in neuronastrocyte interaction, we have differentially analyzed hippocampal Percoll ® -isolated gliosomes and purified synaptosomes using semi-quantitative label-free TripleTOF mass spectrometry. Our results are the first detailed description of the gliosome preparation, and represents an important data source towards understanding of astrocyte membrane proteins and their potential roles in tripartite synapse function.

RESULTS

Gliosomes are enriched for established astrocyte marker proteins.

In order to validate the gliosome procedure, and its comparison with synaptosomes, immunoblotting was used to determine the abundance of well-known astrocyte and synapse marker proteins between homogenate, synaptosome, and gliosome samples from mice 2 -4 months of age. Using a repeated-measures one way ANOVA we found synaptosomes to be GFAP, nor do neuronal spine heads contain the microtubule protein BT3, thus these cytoskeletal proteins should be reduced in gliosomes and synaptosomes when compared to homogenate, respectively [138,190]. Indeed, GFAP was reduced 1.9-fold (F [START_REF] Oberheim | Heterogeneity of astrocytic form and function[END_REF][START_REF] Kettenmann | Neuroglia: the 150 years after[END_REF] = 19.47, p = 0.002) in gliosomes, while BT3 was reduced 2.2-fold (F(1.01, 3.02) = 18.63, p = 0.02) in synaptosomes. Nevertheless, GFAP levels were still higher in gliosomes than in synaptosomes, while BT3 levels were higher in synaptosomes than in gliosomes, which is in accordance with their expected respectively glial and neuronal origin.

TripleTOF mass spectrometry analysis of hippocampal homogenates and Percoll ® -isolated synaptosomes and gliosomes

Five micrograms of protein were analyzed by label free TripleTOF mass spectrometry for each of 5 homogenate, 8 gliosome, and 8 synaptosome samples. In every sample, approximately 3700 different proteins were identified (Supporting Table 1). To remove contaminants and poorly detected proteins a filtering criteria was employed requiring that proteins must be identified in at least 5 of the 8 gliosome or synaptosome samples or 3 of the 5 homogenate samples. This resulted in the elimination of only ~100 proteins per sample group, indicating that the high sensitivity of the TripleTOF allowed for reliable detection of even low abundant proteins (Table 1). Next, significantly enriched proteins were identified using the extreme ratio method (see experimental methods). The distributions of the homogenate-gliosome and homogenatesynaptosome comparisons show a negative enrichment, indicating an overall depletion of proteins, as expected (Figure 3A). For the gliosomes-synaptosome comparison the ratio distribution is centered around zero demonstrating an approximately equal distribution between enrichment and depletion of proteins (Figure 3A). Clustering of the samples based on protein abundance yielded clear separation between experimental fractions with no outlying samples (Figure 3B).

Statistical analysis using the extreme protein ratio method takes the intensity level of proteins into consideration as highly abundant proteins are able to be quantified more precisely.

Poorly detected proteins with large log2 enrichment ratios were less often deemed significant than well-detected proteins with modest log2 enrichment ratios (Figure 3C). The numbers of proteins that were found significantly enriched are presented in Table 1.

Over-representation analysis of gliosomes

We next employed gene ontology analyses to determine whether gliosomes were enriched for proteins involved in particular biological or molecular functions. When gliosomes were compared to the homogenate they were most prominently enriched for proteins involved in 'ribonucleotide catabolic process', primarily comprised of heterotrimeric G-protein subunits (Gs, Gq, Gi, Go G11, G13 , Gz alpha subunits, and β1, β2, and γ2 subunits) and small GTPases (e.g.

Rap1, Rac1, Rho, and CDC42) respectively involved in intracellular transduction of G-protein coupled receptor signaling and cytoskeletal-mediated changes in cell morphology (Table 2, Supporting Table 2). Accordingly, the gliosomes proteome was highly enriched for the molecular function 'GTPase activity' (Table 2, Supporting Table 5). Additionally, marked enrichment was noted in gliosomes for proteins involved in 'regulation of developmental process' and 'cellular developmental process', both consisting in large part of GTPases (Table 2, Supporting Table 5), Gliosome were also enriched in proteins involved in 'cell adhesion' (e.g. cell adhesion molecule 3 and 4, CD9, CD47, Hepacam), and 'ion transport' (e.g. NKCC1, Atp1b1, Atp1b3) (Supporting Table 2).

Synaptosomes were enriched for proteins involved in processes clearly associated with synaptic function ( Table 6). As expected, both synaptosomes and gliosomes were enriched for membrane proteins, and synaptosomes were additionally enriched for synaptic vesicles (Supporting tables [START_REF] Nagelhus | Aquaporin-4 in the central nervous system: cellular and subcellular distribution and coexpression with KIR4.1[END_REF][START_REF] Forsythe | Synaptic transmission: well-placed modulators[END_REF][START_REF] Reichenbach | Morphology and dynamics of perisynaptic glia[END_REF][START_REF] Nico | Morphofunctional aspects of the blood-brain barrier[END_REF].

Next, we determined to which extent gliosomes contain distinct protein sets as compared to synaptosomes. Gliosomes were not enriched for any biological processes when compared with synaptosomes, however gliosomes were enriched for proteins involved in the molecular functions 'receptor binding', 'actin binding', and 'oligosaccharyl transferase activity' (Table 3, Supporting table 7). Proteins involved in 'receptor binding' were mainly heteromeric G-alpha proteins (Gs, Gi, Go, G13 and Gz alpha subunits) but also transmembrane proteins and receptors involved in cell-adhesion (receptor-type tyrosine phosphatase F, EphA4, neuroplastin).

As expected, when compared to gliosomes, synaptosomes showed enrichment for several biological processes, with the most significant enrichment found for proteins involved in 'neurotransmitter transport', and 'vesicle mediated transport' (Table 3, Supporting table 4).

Cumulatively the above analysis shows that gliosomes are enriched for proteins involved in G-protein mediated signaling, cytoskeletal rearrangements, and cell adhesion, which is in line with gliosomes containing astrocyte membrane-associated proteins. Furthermore, gliosomes were depleted of proteins prominently enriched in synaptosomes, such as proteins involved in synaptic transmission and synaptic vesicle release.

Immunoblot validation of mass spectrometry-based analysis

Proteins identified as significantly enriched or depleted in gliosomes compared to synaptosomes by mass spectrometry (FDR ≤ 10% FDR), were evaluated by immunoblot on independent replicates (n = 12, 6 pairs). GFAP is a well-established astrocyte marker and both aldolase C (Aldoc, a glycolytic enzyme) and basigin (Bsg, a membrane glycoprotein), have previously been characterized as being astrocyte-specific [191]. Our mass spectrometry analysis identified all 3 proteins as being significantly gliosome-enriched compared to synaptosomes. Immunoblotting confirmed the significant enrichment of basigin (1.4-fold, p = 0.01) and GFAP (4.3-fold, p = 7.5 x 10 -5 ), while aldolase C showed a very strong trend towards enrichment (1.3-fold, p = 0.06, Figure 4A). Furthermore, mass spectrometry indicated that ankyrin 2 (Ank2, a membrane-cytoskeleton adaptor protein), myelin basic protein (MBP, the primary constituent of the myelin sheath), and neurofilament heavy chain (Nf-H, an intermediate filament) were highly enriched in gliosomes as well, which was confirmed with immunoblotting (enrichment of Ank2 (1.3-fold, p = 0.01), MBP (2.1-fold, p = 0.004), and Nf-H

(1.6-fold, p = 0.005)). The astrocytic glutamate transporter GLT-1 was found at comparable levels in both gliosomes and synaptosomes. On the other hand, the neuronal cytoskeleton protein beta-tubulin III and the mitochondrial protein enoyl coenzyme A hydratase 1 (Echs1)

were significantly depleted in gliosomes when compared to synaptosomes, as determined by both mass spectrometry and immunoblotting (synaptosome enrichment 1.9-fold, p = 0.02 and 2.9-fold, p = 4.4 x 10 -6 respectively, Figure 4B). Immunoblots for known neuronal and glial marker proteins performed on homogenate, synaptosome, and gliosome samples also corresponded with the protein enrichments identified by mass spectrometry (Supporting Figure 1). Overall these results validate the mass spectrometry analysis and show that gliosomes are enriched for glial proteins, while being depleted of synaptosomal proteins.

DISCUSSION

Gliosomes are enriched for membrane-associated proteins

The increasing recognition of the importance of astrocytes to brain function necessitates advances in techniques to purify astrocyte protein from acutely isolated brains for further analyses. Recently established methods to purify astrocytes from in vitro neuron-astrocyte co-cultures [192] and to isolate entire astrocytes from brain tissue [193,194] are available, however, a robust method for the specific isolation of astrocyte proteins in vivo remains highly desirable.

Here we used mass spectrometry to evaluate the protein content of gliosomes to characterize the suitability of this preparation for further study of astrocyte proteins. We found that gliosomes are enriched for proteins involved in astrocyte exocytosis machinery (based on the marker protein VAMP3 [178]), perisynaptic astrocyte processes (based on the marker protein Ezrin [137,138]), and astrocyte plasma membrane proteins (e.g. the astrocyte membrane glycoprotein Basigin [195]). In line with studies on functional analysis of release and uptake of various neurotransmitters of gliosomes [185-187], we found gliosomes to contain glutamate transporters while being depleted of synaptosomal proteins, such as the presynaptic SNARE proteins and post-synaptic NR1 and PSD-95. In addition, gene ontology analyses of gliosomal proteins revealed that gliosomes are primarily of membrane origin and enriched for heteromeric G-protein subunits and small GTPases. A wide range of G-protein coupled receptors (GPCRs) have been detected in perisynaptic astrocytes [START_REF] Porter | Astrocytic neurotransmitter receptors in situ and in vivo[END_REF]. Activation of these receptors can lead to an increase in intracellular calcium which can subsequently trigger gliotransmitter release and thereby modulate both excitatory and inhibitory synaptic transmission [START_REF] Santello | Gliotransmission and the tripartite synapse[END_REF][START_REF] Kang | Astrocyte-mediated potentiation of inhibitory synaptic transmission[END_REF][START_REF] Navarrete | Endocannabinoids potentiate synaptic transmission through stimulation of astrocytes[END_REF]. The G-proteins enriched in gliosomes may be components of this complex cascade. Interestingly, heterotrimeric G-proteins in human have been genetically associated with cognitive ability, which was suggested to involve alterations in neuronal networks in the brain [196]. These alterations may involve astrocyte heterotrimeric Gproteins, with gliosomes providing a suitable membrane preparation for molecular and functional analysis of heterotrimeric G proteins.

Interestingly, our observation that gliosomes are enriched for the PAP markers Ezrin and VAMP3, suggest that gliosomes are a suitable preparation to study PAP proteins. These perisynaptic astrocyte processes (PAPs) are highly motile and extremely fine astrocytic peripheral processes that are found to ensheath synapses in many brain regions, including hippocampus [125], hypothalamus [105,138], and cerebellum [118], furthermore ideally poised to exert modulatory control over synaptic activity [START_REF] Henneberger | Long-term potentiation depends on release of D-serine from astrocytes[END_REF]126]. It should however be noted that we also found PAP proteins present in synaptosomes, which is in line with previous reports that synaptosomes contain an abundance of glial proteins [197,198]. PAP proteins may well be derived from the intimate connection of astrocytic and synaptic membranes.

Taken together, the results contain the first detailed description of gliosomal proteins, and indicate that gliosomes are enriched for astrocyte membrane proteins, including astrocyte proteins related to perisynaptic function. Together, this quality allows gliosomes to be used for expression analysis of astrocyte proteins in manipulation paradigms including knock-out animals, behavioral paradigms, and drug treatments to identify novel roles of astrocytes in brain function.

EXPERIMENTAL PROCEDURES

Mice

All experiments were conducted with approval of the Italian Ministry of Health Ethical Committee. Adult (2-4 months) C57BL6/J mice from Charles River (Italy, bred in-house) were used for the generation of Percoll ® -isolated synaptosomes and gliosomes.

Purification of Percoll ® -isolated gliosomes and synaptosomes

Animals were euthanized and the hippocampus was rapidly dissected on ice. Purified gliosomes and synaptosomes were obtained essentially as previously described [184], with minor modifications (Figure 1). In detail, the hippocampi from two mice were pooled and homogenized in 2 ml of 0.32 M sucrose, buffered at pH 7.4 with Tris (0.1 M)-HCl using 12 strokes at 900 rpm with a glass-teflon tissue grinder (clearance 0.25 mm -Potter-Elvehjem VWR International). The homogenate was centrifuged (5 min, 1,000 x g at 4°C) to remove nuclei and debris. The supernatant (S1) was collected and centrifuged (5 min, 12,000 x g at 4°C) to avoid contamination by small inert vesicles. The pellet (P2) was resuspended in 1ml of 0.32 M Tris-buffered sucrose, gently stratified on a discontinuous Percoll ® gradient (3 mL of 2, 6, 10, and 20% v/v Percoll ® in 0.32 M Tris-buffered sucrose) and centrifuged at 33,500 x g for 6 min. No more than 2 mL of suspension, corresponding to a maximum of 0.3 g tissue, was layered into each gradient tube. The layers between 2% and 6% Percoll ® (gliosomal fraction) and between 10 and 20% Percoll ® (synaptosomal fraction) were collected. The samples were washed twice in 30-40 mL of phosphate-buffered saline (PBS) by centrifugation at 20,000 x g for 7 min. The gliosomal and synaptosomal precipitates were then resuspended in 25 mM HEPES for further analysis. Gliosomal and synaptosomal fraction from 2 isolations were pooled together to generate samples with protein yield representing 4 animals In this method, gliosomes are considerably less abundant than synaptosomes, yielding approximately 10-fold less protein (for hippocampi from 4 mice, ~60 ug of gliosomes and ~600 ug of synaptosomes).

In-gel digestion

In-gel digestion was performed as previously described [199] with some modification. Eight gliosome, eight synaptosome, and five homogenate samples were analyzed by mass spectrometry. For all groups, 5 µg of each sample was diluted in SDS sample buffer and denatured for 5 minutes at 98 °C. To block cysteine residues, 2 uL of 30% acrylamide was added to each sample and allowed to incubate for 30 minutes. Samples were run on 10% SDS-PAGE gels just until all proteins had passed through the stacking gel. Gels were fixed overnight and stained with Coomassie blue to visualize all proteins. Each sample lane was cut into 2 pieces through the 50 kD marker to reduce overall sample complexity. Each gel piece was further cut into ~1 mm 3 blocks and placed in a 96 well filter plate (Millipore). A collection plate (Eppendorf) was placed below the filter plate so that after spinning plates for 1 minute at 200g waste solutions could easily be discarded. A fresh collection plate was used for the peptide elution phase. Destaining of the gel slices was achieved by two rounds of incubation in 50% acetonitrile/50 mM ammonium bicarbonate, dehydration by 100% acetonitrile, and rehydration in 50 mM ammonium bicarbonate. After the second acetonitrile dehydration gel pieces were rehydrated in 50 mM ammonium bicarbonate containing 300 ng trypsin/Lys-C (Promega, Mass Spec grade) and incubated at 37 °C for 18 hours. Peptides were extracted from the gel pieces with 2, 20 minute rounds of incubation in 50% acetonitrile/ 0.1%

Trifluoroacetic acid and a final round in 80% acetonitrile/ 0.1% Trifluoroacetic acid. The extracted peptide solution was dried in a SpeedVac and stored at -20 °C until mass spectrometry analysis.

TripleTOF MS/MS

Peptides were analyzed by nano LC-MSMS using an Ultimate 3000 LC system (Dionex) coupled to the TripleTOF 5600 mass spectrometer (AB-Sciex). Peptides were trapped on a 5 mm Pepmap 100 C18 column (300 µm ID, 5 µm particle size, from Dionex) and fractionated on a 200 mm Alltima C18 column (100 µm ID, 3 µm particle size). The acetonitrile concentration in the mobile phase was increased from 5 to 30 % in 90 min, to 40% in 5 min and to 90% in another 5 min, at a flow rate of 400 nL/min. The eluted peptides were electrosprayed into the TripleTOF MS (5600 from AB-sciex). The mass spectrometer was operated in a data-dependent mode with single MS full scan (m/z 350-1200)

followed by top 20 MS/MS scan.

Protein identification and quantitation

Spectrum annotation and relative protein quantification were performed with Peaks 7

software [200]. Searches were performed against the Swissprot database of canonical sequences (version 03-2014) for trypsin-digested peptides with a parent mass error tolerance of 25 ppm and a fragment mass error tolerance of 0.025 Da. We allowed for 2 missed cleavages, fixed proprionamide modification and variable modifications of N-terminal acetylation, oxidation of methionines, and deamination. Only spectra matched to a peptide with an FDR of less than 1% were used for protein quantification. To correct for variations in input material, intensities were normalized on total ion current.

Identification of significantly enriched proteins

Significantly regulated proteins were determined by identification of significantly large protein ratios of averaged protein abundance values against a background of inferred experimental variation as modified from Cox & Mann [201].

As an estimate of experimental variation, background log2-ratios of the same experimental group (gliosome, synaptosome, homogenate) were calculated for each protein. For the gliosome vs. synaptosome comparison this ratio was calculated as the median of four gliosome vs. gliosome and four synaptosome vs. synaptosome log2-ratios. For the gliosome vs. homogenate and synaptosome vs. homogenate comparison this ratio was calculated as the median of two homogenate vs. homogenate log2-ratios and four gliosome vs. gliosome or four synaptosome vs. synaptosome log2-ratios, respectively. Median protein log2-ratios were plotted as a function of the summed MS1 spectral intensity of the respective protein across samples in the pairwise comparison. Bins of MS1 spectral intensity were defined so that each included at least 300 protein ratios, and normal distributions for each bin were fitted as a model of experimental variation (null distribution).

Next, foreground log2-ratios were calculated that represent experimental and biological variation. For the gliosome vs. synaptosome comparison this ratio was calculated as the median of eight gliosome vs. synaptosome protein log2-ratios. For the gliosome vs.

homogenate and synaptosome vs. homogenate comparison this ratio was calculated by subtracting the median protein abundance in five homogenate samples from the median protein abundance of the eight gliosome or synaptosome samples, respectively.

Finally, a p-value for differential protein expression in a pairwise-comparison for each protein was established as the probability that the foreground protein log2-ratio has been sampled from the null distribution of the corresponding bin of protein abundance in the pairwisecomparison (calculated using the Z-score). P-values were corrected for multiple testing according to Benjamini and Hochberg [202] for each pair-wise comparison individually and proteins within a FDR of 10% were considered significantly regulated. To filter out infrequently observed proteins, only proteins that were quantified in at least 50% of the samples of at least one of the experimental groups in a pair-wise comparison were included in the list of significant proteins.

Hierarchical clustering

Hierarchical clustering of samples was performed using Euclidean distances and Ward's method of agglomeration [203].

Immunoblotting

Four sets of homogenate-gliosome-synaptosome samples were used for the marker enrichment analysis, and were also analyzed by mass spectrometry. Immunoblotting for the validation of the mass spectrometry was performed on six independent gliosomesynaptosome pairs. The protein concentrations of all samples were determined by Bradford assay (BioRad). Proteins were diluted in SDS sample buffer and denatured by heating to 98 °C for 5 minutes. Samples were separated on Criterion 4-15% TGX Stain-Free precast gradient polyacrylamide gels (BioRad) and then transferred to PVDF membrane (BioRad). Membranes were blocked in 5% skim milk-Tris-buffered saline/ 0.05% Tween 20 (TBS-T) and incubated overnight with primary antibody at 4 °C. Primary antibodies against beta-tubulin III (1:2,000, Sigma), PSD95 (1:10,000, Neuromab), GluN1 (1:1,000, Neuromab), GFAP (1:1,000, Dako),

Ezrin (1:1,000, GenScript), VAMP3 (1:500, AbCam), Basigin (1:1,000, Santa Cruz), Ankyrin2

(1:500, Neuromab), GLT1 (1:2,000, Millipore), Echs1 (1:500, Pierce), Glyoxylase1 (1:1,000, Santa Cruz), Aldoc (1:1,1000, Santa Cruz), and Ndrg2 (1:200, Santa Cruz) were used.

Appropriate polyclonal HRP-conjugated secondary antibodies were applied for 1 hr RT (Dako).

Probed membranes were incubated with SuperSignal West Femto Chemiluminescent substrate (Thermo-Scientific) and the reaction was imaged with a Li-Cor Odyssesy 2800 scanner. Blots were quantified using ImageStudio with background correction. To visualize initial protein loading for quantification normalization, gels were activated with UV-light prior to transfer using Gel Doc EZ Imager. ImageLab was used to quantify the optical density of the total protein content of each sample on the gel that was then used as a loading control for the corresponding immunoblot.

Statistical analyses were performed with SPSS 21 (IBM). Blots evaluating the enrichment of marker proteins compared to the homogenate were analyzed with a repeated-measures ANOVA and subsequent post-hoc analysis was performed with Fisher's least significant difference (LSD) tests. The Greenhouse-Geisser correction was used for analyses that failed to meet the sphericity criteria. Blots to validate mass spectrometry results were evaluated with a one-tailed paired Student's t-test.

Gene ontology analysis

The Bingo plug-in (v2.44) for Cytoscape (v2.8.3) was used to perform a hypergeometric test to assess over-representation of GO terms for biological process, molecular function, and cellular component. Statistically significant (p≤ 0.05 after correction with Benjamini and Hochberg False discovery Rate) over-representation of GO terms was derived from comparison with a reference set of all proteins detected by mass spectrometry in the hippocampal homogenate. Due to the high degree of overlap between significantly-enriched terms, we employed a set of filtering criteria to reduce the list to its most concise representation. Three guidelines were followed: 1) select terms with the lowest p-value 2) at least 50% of proteins in a term must be unique to that term, 3) terms must have at least 3 members.

ACKNOWLEDGMENTS

The mass spectrometry proteomics data have been deposited to the ProteomeXchange

ABSTRACT

Astrocytes in the supraoptic nucleus (SON) of the hypothalamus demonstrate prominent temporary structural changes during physiological events such as dehydration and lactation.

Such structural remodeling is characterized by a reduction in the astrocytic coverage of oxytocin neurons and their synapses, which contributes to the observed functional changes in synaptic activity during dehydration and lactation. In order to elucidate the underlying mechanisms of astrocytic contributions to synaptic plasticity we investigated the astrocyte protein expression profile for structural changes in the SON during lactation and hyperosmotic challenge. For this, SON from virgin, lactating, and hyperosmotic rats were collected from acute hypothalamic slices and analyzed by mass spectrometry to identify proteins differentially regulated by the changes in synaptic structure induced by lactation and hyperosmolarity. Our mass spectrometry data analysis placed particular focus on regulation of astrocyte-specific proteins. Immunoblotting was used to validate the mass spectrometry data. A reproducible up-regulation of Ndrg2 under both lactating and hyperosmotictic condition was detected, however, large discrepancies were found for most proteins. The difficulty to reproducibly find expression changes for SON proteins during SON structural remodeling might be explained by its rapid dynamic nature. We discuss possible modifications to the experimental design to enable successful identification of more astrocyte proteins involved in this structural plasticity.

INTRODUCTION

The supraoptic nucleus (SON) is part of the hypothalamic-neurohypophysial system. The SON is densely packed with vasopressin and oxytocin magnocellular neurons interspersed with thin astrocytic processes. The neuropeptide vasopressin is involved in the regulation of water balance whereas oxytocin primarily participates in the orchestration of reproductive processes including parturition and the milk ejection reflex, but has an additional role in water homeostasis [205][206][207]. Axons from the magnocellular neurons project through the internal layer of the median eminence directly to the neurohypophysis allowing oxytocin and vasopressin to be directly secreted into the blood stream upon physiological demand [208,209].

The anatomical restructuring capacity of the supraoptic nucleus (SON) has been used as a model of structural and functional plasticity for decades [210,211]. During conditions of physiological stimulation such as dehydration, hyperosmosis, and lactation, activation of the nucleus results in hypertrophy of neuronal somas and the retraction of the glial processes ensheathing synapses. This retraction allows for juxtaposition of more neuronal components, leading to the formation of additional synapses and even 'double synapses', where one axon terminal forms a synapse with two different post synaptic cells [212]. Interestingly, the structural remodeling and consequently increased number of double synapses, increased extent of neuronal apposition, and decreased percent of glial contact per neuron is observed only for oxytocin neurons, not vasopressin neurons. This remodeling is observed to equal extents in both hyperosmosis/dehydration and lactation [START_REF] Chapman | Osmotic stimulation causes structural plasticity of neurone-glia relationships of the oxytocin but not vasopressin secreting neurones in the hypothalamic supraoptic nucleus[END_REF][START_REF] Theodosis | Structural plasticity in the hypothalamic supraoptic nucleus at lactation affects oxytocin-, but not vasopressin-secreting neurones[END_REF]. Additionally, this structural plasticity is complete reversible as after the end of lactation or upon rehydration the astrocytic coverage of neurons and number of synapses returns to normal [213,214].

The reduced synaptic coverage also effectively removes the physical barrier between synapses, facilitating diffusion of all signaling molecules within the nucleus. This is particularly true for glutamate, whose uptake is delayed under stimulated conditions since transporters ensuring its clearance are located on the retracted astrocytic processes. Glial withdrawal thus causes a local buildup of the excitatory amino acid and consequently an enhanced activation of presynaptic group III metabotropic glutamate receptors (mGluRs) that are negatively coupled to glutamate release [106]. One of the main outcome of the reduced astroglial coverage is thus a reduced efficacy at excitatory inputs due to an enhanced negative feedback provided by glutamate onto its own release. The glial withdrawal also allows spillover of glutamate from one synapse to another, leading to functional alterations on distant terminals.

Increased levels of synaptic glutamate after high levels of neuronal firing can diffuse and activate presynaptic group III mGluRs on neighboring GABAergic neurons, resulting in decreased inhibitory signals [START_REF] Piet | Physiological contribution of the astrocytic environment of neurons to intersynaptic crosstalk[END_REF]. Several previous studies have sought to identify genes and proteins involved in the regulation of this dynamic process using models of dehydration, hyperosmosis, and/or lactation. For instance, poly-sialyated neuronal cell adhesion molecule (PSA-NCAM) was found highly expressed by perisynaptic astrocyte processes in the SON, and the presence of the PSA modification, which inhibits cell adhesion, was required for the induction of SON structural plasticity in both hyperosmosis and lactation models [217]. Microarray studies comparing the similarities of dehydration-induced transcriptome changes between mice and rats have reported regulation of genes involved in polyamine synthesis, prostaglandin E2 signaling, and extracellular matrix remodeling in both species, suggesting that evolutionarily conserved pathways are likely to be important to SON physiology [218][219][220]. Two-dimensional difference gel electrophoresis (2D-DIGE) proteomic analysis of dehydrated rat SON identified the regulation of 3 proteins by dehydration: heat-shock protein 1 alpha (Hsp1a), neuronal axonal membrane protein (NAP22), and protein-disulfide isomerase 3 (Pdia3). However, the mechanism via which these proteins could affect structural plasticity remains to be elucidated [221].

Each of these studies has contributed a piece to the puzzle but surprisingly there is very little overlap in the genes and proteins so far proposed to be involved in this dynamic process. This may be due to differences in experimental protocols, poor correlation between gene transcript levels and protein levels, and reported strain-dependent variations in rat hypothalamic-neurohypophyseal transcriptomes [222]. Here we set out for a more definitive answer about the underlying mechanisms of this remodeling, by performing a large-scale protein-level analysis in both lactation and hyperosmosis models. Because lactation and hyperosmosis are robust physiologically stimulating paradigms on their own, we hypothesized that many proteins would change levels for each of the models, however, that proteins which are similarly regulated in both models would be most likely be involved in the structural plasticity, as this is shared between both conditions.

We thus conducted a label-free semi-quantitative mass spectrometry analysis of SON isolated from virgin, lactating, and hyperosmotic female rats. Clustering of the identified proteins, based on the patterns of expression-level changes, allowed us to identify groups of proteins that behaved similarly in both plasticity-inducing paradigms. Unfortunately, we were unable to validate most of the mass spectrometry results by immunoblotting. Further analyses suggested that our selected approach likely contained too much biological and technical variation to achieve the desired aims. However, the overall principle of the experiment is likely to remain valid and with some modifications to the experimental design, this approach is promising for identification of astrocyte-proteins involved in SON structural plasticity.

RESULTS

Mass spectrometry analysis of SON from virgin, hyperosmotic, and lactating rats

To identify SON proteins regulated by plasticity, a crude membrane fraction (P2+M) was isolated from the supraoptic nuclei of a pool of 3 female rats of either the virgin, hyperosmotic, or lactating rats. Four samples of each group were analyzed by label free quantification on an Orbitrap mass spectrometer. A total of 3363 proteins were detected that met the requirement of having peptides present in at least 2 samples. To determine if any proteins were significantly regulated, all 3363 proteins were subjected to statistical analysis with SAM to generate false-discovery rates (FDRs). The FDRs allows one to estimate the level of false-discoveries (incorrectly rejected null hypotheses) present in a subset of the data, and enables one to select a collection of proteins that are consistent with a desired level of falsediscoveries. Because we were interested in conducting a large-scale screen to identify many possible targets and associated changes in biological function, we accepted a relatively high false discovery rate (FDR) of 22% for our statistical analyses. Multiclass analysis of all 3 groups resulted in 985 proteins regulated with a false discovery rate (FDR) of 22% or less (Figure 1A).

We also conducted a pairwise statistical analysis for virgin versus hyperosmotic and virgin versus lactating. A total of 1,040 proteins were different between hyperosmotic and virgin with a FDR of 21% while only 48 were different between lactating and virgin at 30% FDR (Figure 1 B,C). Protein numbers between these 3 groups were compared at slightly different FDRs because FDRs are generated in a step-wise rather than continuous fashion, thus it is often not possible to compare exactly the same FDR between different analyses.

The striking difference in numbers of proteins regulated in hyperosmotic versus lactating samples led us to further investigate the sample clustering patterns. Virgin, hyperosmotic, and lactating samples were clustered as groups as well as by individual samples using Pearson correlation coefficient-derived distance. Group clustering confirmed that virgin and lactating groups were much more similar to each other than they were to the hyperosmotic group, indicating the hyperosmotic protocol is a more robust model (Figure 1D). Clustering of the individual samples revealed 2 primary sub-clusters (Figure 1E). Hyperosmotic samples form a distinct, tight sub-cluster and virgin samples form a separate sub-cluster. However, half of the lactating samples clustered with hyperosmotic samples and the other half grouped with virgin samples. This high degree of variation between lactating samples may explain the low number of significantly regulated proteins between virgin and lactating groups.

Identification of clusters of plasticity-related protein regulations

In this paradigm we expect five possibilities for patterns of protein regulation: 1) proteins that are not regulated at all, and thus not present in the list of significantly regulated proteins, 2)

proteins regulated in only the hyperosmotic, or 3) proteins regulated in only the lactating condition, thus not related to a plasticity mechanisms shared by both conditions, 4) proteins differentially regulated in each experimental model, and most importantly, 5) proteins regulated in the same direction in both paradigms and therefore possibly involved in the SON structural remodeling that occurs in both conditions (Figure 2A). In order to elucidate the proteins which were most likely to be plasticity-related, we used Euclidean distance to cluster all 985 proteins regulated at 22% FDR in the multiclass analysis. A total of 16 clusters were generated with each colored line representing the average expression level of a single protein across virgin, hyperosmotic, and lactating samples (Figure 2B). From these 16 clusters, we selected the 8 that exhibited a pattern most indicative of a role in plasticity (578 proteins).

Clusters demonstrating increased expression in both hyperosmotic and lactating rats compared with virgins (green boxes, 329 proteins) were termed "up-regulated clusters" whereas those with decreased expression compared with virgins were designated as "downregulated clusters" (red boxes, 249 proteins).

Distinguishing the contribution of astrocytes to SON plasticity

Our primary interest is in the role of astrocytes in plasticity. Because our analysis was performed on a membrane fraction from all cell types present in the SON, an additional level of filtering was required to determine which of these proteins are of astrocytic origin. To this end, the list of plasticity proteins was overlapped with a manually curated list of 1998 astrocyte-enriched genes [191], which resulted in the identification of 86 astrocytic plasticity proteins (Figure 3).

To further characterize these astrocyte proteins, a gene ontology over-representation analysis for biological process was performed. 'Small molecule metabolic process' was the most significant term, which contained many transmembrane transporters (including those for GABA, water, choline, and neutral amino acids). In addition, overrepresentation was found for multiple metabolic functions including carboxyl acid and lipid metabolism (Table 1).

Validation of mass spectrometry results by immunoblotting

Astrocyte proteins

Astrocyte plasticity proteins from the over-represented GO terms 'small molecule metabolic process', 'lipid metabolic process', and 'transport' were selected for validation of mass spectrometry results. Protein expression levels were evaluated in 5 independent samples from each experimental group.

For the proteins aldolase C (Aldoc) and Na + -K + transporting ATPase subunit alpha 2 (Atp1a2), both present in the 'small molecule metabolism', mass spectrometry analysis predicted that both proteins should be up-regulated by plasticity conditions. However, immunoblots did not confirm the regulations for Atp1a2 (F(2,12) = 0.50, p= 0.62) or Aldoc (F(2,12) = 2.36, p = 0.14), although Aldoc regulations were in the correct direction (Figure 4A). Also for the 'lipid

Proteins with low FDR or high fold changes

For most of the astrocyte proteins we attempted to validate, fold changes were < 2 with 22% FDR. To explore the possibility that validation failed because the measurements were too uncertain, we next blotted for proteins with low FDRs or high fold-changes. The mass spectrometry results were sorted by FDR and fold change, and high ranking proteins, for which an antibody was available, were selected for immunoblot analysis. (Figure 4B). Ezrin was measured by mass spectrometry to show a 2.9 -fold increase (45% FDR) in the hyperosmotic condition. By immunoblot analysis ezrin regulation was significant (F(2,12) = 4.75, p = 0.03), but the regulation was opposite of the mass spectrometry results with a decrease in ezrin expression between virgin and lactating samples (-1.19 -fold, p = 0.01). The protein receptor-type tyrosine-protein phosphatase F (Ptprf) had a 0% FDR in the MS results and was detected in virgin condition samples, though not in any hyperosmotic or lactating condition samples, suggesting a strong decrease in expression by plasticity. Immunoblot analysis showed no difference in expression levels of this protein in any condition (F(2,12) = 0.55, p = 0.59). Thus, we were also not able to satisfactory validate the mass spectrometry results for proteins with low FDR or high fold changes.

Proteins regulated only by hyperosmosis

Our initial bioinformatics analysis indicated that very few proteins were significantly regulated by lactation, so perhaps including all these samples in our multiclass analysis masked true changes present in the more robust virgin-hyperosmotic comparison. To determine whether we could validate any proteins regulated by only hyperosmosis we selected proteins with FDRs less than 10% in the virgin-hyperosmotic pairwise comparison, regardless of their degree of astrocyte expression. Protein disulfide isomerase (PDI), Claudin-11 (Cldn11), and vesicle-fusing ATPase (NSF) were expected to be up-regulated by hyperosmosis, but immunoblotting showed no significant difference between groups (PDI: F(2,9) = 0.12, p = 0.88;

Cldn11: F (2,12) = 0.18, p = 0.83 ; NSF: F (2,12) = 0.27, p = 0.27). N-myc downstream regulated gene 2 (Ndrg2), an astrocyte-specific gene, was predicted by mass spectrometry to be 1.

Proteins known to be regulated by SON plasticity

As a final validation step we wanted to assess whether our samples, regardless of mass spectrometry results, are in accordance with the literature regarding protein level changes that have previously been observed after SON stimulation. Protein disulfide isomerase A3 (Pdia3/GRP58) was previously found to be significantly up-regulated in the SON after 3 days of dehydration [221]. Although our mass spectrometry results also suggested an increase in Pdia3 after SON stimulation, we were unable to validate this finding by immunoblotting (F [START_REF] Oberheim | Heterogeneity of astrocytic form and function[END_REF][START_REF] Nico | Morphofunctional aspects of the blood-brain barrier[END_REF] = 0.91, p = 0.43, Figure 5B). Tyrosine hydroxylase is considered a marker of vasopressin neuron activation and increased expression levels are seen in the SON upon osmotic challenge [223]. Whereas tyrosine hydroxylase was not reliably detected with mass spectrometry, immunoblotting for tyrosine hydroxylase showed a significant regulation (F(2,12) = 5.02, p = 0.02), however, this was for a decrease in expression in hyperosmotic compared with virgin rats (-1.92 -fold, p = 0.009) with a trend toward reduction in lactating compared with virgin rats (1.52 -fold, p = 0. 07, Figure 5B). Representative immunoblots with approximate molecular weights and loading controls appear below the corresponding bar graph quantification. A) Expression levels of significantly regulated proteins were evaluated on the same samples used for mass spectrometry analysis. Protein regulations found by mass spectrometry were not confirmed, however, Nrdg2 showed a trend towards up-regulation in lactation (F(2,9) = 2.99, p = 0.10, post-hoc analysis V-L p = .05). B) Based on literature, expression levels of Pdia3 and TH were expected to be upregulated in both plasticity conditions. TH was significantly down-regulated by plasticity compared with virgin animals. Bar graphs depict the mean log2 expression ratio relative to virgin samples ±SEM. * p ≤ 0.05.

DISCUSSION

Plasticity-related proteins in the SON

To discover novel astrocyte proteins involved in structural plasticity we selected the wellknown model of function-dependent plasticity in the supraoptic nucleus. Crude membrane fractions from SON of virgin, lactating, and hyperosmotic animals were analyzed by mass spectrometry to identify proteins which were regulated in the same direction in both experimental paradigms. To our knowledge, this is the first large-scale proteomics screen comparing both hyperosmotic and lactating animals using a label-free quantitation approach.

A total of 578 proteins meeting the plasticity-related expression profile were considered for further analysis.

One protein, n-myc downstream-regulated gene 2 (Ndrg2), showed up-regulations in hyperosmotic and lactating animals by both mass spectrometry and immunoblotting. Ndrg2

has been identified as an astrocyte-specific protein expressed in the cell body as well as in the perisynaptic astrocyte processes [224]. Although its exact functions remain largely unknown, expression levels of Ndrg2 in astrocytes have been shown to increase with exposure to estrogen [225]. Remarkably, we found increased levels of Ndrg2 in the post-partum period, when estrogen levels are known to drop [226,227]. Expression levels of Ndrg2 have also been shown to affect astrocyte process morphology, with decreased Ndrg2 expression resulting in reduced process length [228]. Although the percentage of neural soma profiles ensheathed by astrocyte processes is decreased during hyperosmosis and lactation, the absolute length of glial processes is actually increased due to the hypertrophy of mangocellular neurons [START_REF] Chapman | Osmotic stimulation causes structural plasticity of neurone-glia relationships of the oxytocin but not vasopressin secreting neurones in the hypothalamic supraoptic nucleus[END_REF][START_REF] Theodosis | Structural plasticity in the hypothalamic supraoptic nucleus at lactation affects oxytocin-, but not vasopressin-secreting neurones[END_REF]. Therefore, the increased expression of Ndrg2 and probable increase in astrocyte process length is in line with the structural changes induced by SON stimulation, suggesting Ndrg2 may facilitate these morphological rearrangements.

However, it should be noted that although the morphological plasticity of the stimulated SON is often referred to as "glial retraction", it is not yet known if astrocytes actively retract from their neuronal contacts. Vassopressin neurons in the SON also hypertrophy during stimulation, but the percentage of their surface covered by glia processes remains the same as glial membrane extend to compensate neuronal swelling [START_REF] Chapman | Osmotic stimulation causes structural plasticity of neurone-glia relationships of the oxytocin but not vasopressin secreting neurones in the hypothalamic supraoptic nucleus[END_REF][START_REF] Theodosis | Structural plasticity in the hypothalamic supraoptic nucleus at lactation affects oxytocin-, but not vasopressin-secreting neurones[END_REF]. This may suggest that the glial processes ensheathing oxytocin neurons fail to extend in response to neuronal hypertrophy rather than actively retract, and thus should be thought of more generally as astrocyte reorientation [229].

Sources of variation in the experimental paradigm

Although we found many significant regulations by mass spectrometry between virgin and hyperosmotic animals, few were identified between virgin and lactating animals. These data suggest hyperosmosis is a considerably more robust paradigm than lactation. Furthermore, this indicates that the use of virgin and lactating rats from different companies (Charles River and Harlan, respectively) did not result in noticeable SON proteome changes. However, most of the regulations that were measured could not be validated using immunoblotting on independent replicates or on the same replicates used for mass spectrometry analysis.

Similarly, regulation of positive control proteins (Pdia3 and TH) also could not be confirmed by immunoblotting. Mass spectrometry analysis detected increased levels of Pdia3 in both hyperosmotic and lactating rats (multiclass FDR 18%), which is in accordance with the literature using a model of dehydration [221], although immunoblot analysis did not confirm this up-regulation. However, because dehydration is a more complex stimulus than hyperosmosis, involving both hypertonicity and hypervolemia, the lack of up-regulation in our model may indicate that Pdia3 regulation is specific to hypervolemia. Additionally, the reported up-regulation of the cytosolic protein TH by hyperosmosis had been assesses by immunohistochemistry [230]. As we conducted immunoblot analyses on samples enriched for membrane proteins, the levels of TH present may not accurately represent the original cell contents. Nonetheless, overall we found poor correlation between mass spectrometry and immunoblot data. This indicates that a high degree of variation was present in our experimental paradigms and/or technical approaches, which may underlie the nonreproducible and conflicting results.

Temporal variation

Recent reports have indicated that protein expression level changes in the SON can happen Osmoregulatory mechanisms of the body strive to maintain stable levels of extracellular fluid osmolarity. However, fluctuations around this set-point occur as a result of daily variations in fluid intake, natriuresis, and perspiration [START_REF] Bourque | Central mechanisms of osmosensation and systemic osmoregulation[END_REF]. Consumption of hyperosmotic solution induces a small fluctuation in portal venous osmolarity, and subsequently a compensatory activation of the SON, mediated in part by activation of oxytocin magnocellular neurons [207,235].

Activation of the SON and stimulation of oxytocin release with each fluctuation in blood osmolarity may require induction of structural plasticity mechanisms and concomitant alterations in protein expression. Because we did not monitor how long after their last water consumption the rats were sacrificed, the protein regulations observed may not correlate to the model as expected. Similarly, rapid changes in protein expression have been documented

for the SON of lactating animals. GFAP expression during the suckling period significantly decreases within minutes, but returns to baseline levels after the milk-ejection reflex has occurred [231]. As we did not monitor the precise timing of each nursing before sacrificing the lactating mothers, this may have resulted in variation between the lactating rats for acute suckling-mediated changes in the SON. Together these observations suggest that rapid changes in protein level expression can occur with fluctuation in osmolarity and lactation timing that may be overlooked if the time of animal sacrifice is not closely monitored.

It should be noted, however, that electron microscopy has shown an approximately 10% decrease of astrocytic coverage of SON neurons during lactation and dehydration, indicating there is some level of constant structural difference between virgin/euhydrated animals and stimulated animals [START_REF] Chapman | Osmotic stimulation causes structural plasticity of neurone-glia relationships of the oxytocin but not vasopressin secreting neurones in the hypothalamic supraoptic nucleus[END_REF][START_REF] Theodosis | Structural plasticity in the hypothalamic supraoptic nucleus at lactation affects oxytocin-, but not vasopressin-secreting neurones[END_REF]. The effects of this structural plasticity can also be detected by functional assays [106,107]. The electron microscopy studies may represent a snapshot of an even more dynamic anatomical remodeling process that occurs in vivo, which is dependent on de novo protein synthesis [236]. Proteins with stably altered expression levels by stimulation, like Ndrg2, may represent permissive factors to the required dynamic changes.

Subcellular fractionation technique

Due to the small size of the SON, we were required to process samples into a crude membrane fraction rather than a more purified synaptosomal fraction. The use of a P2+M fraction may have masked protein level changes present specifically at the synapse (including perisynaptic astrocyte processes), which might have been detected when using sucrose-isolated synaptosomes (Chapter 3). On the other hand, with less astrocytic coverage during stimulation conditions it is possible that astrocytic PAP proteins would be well detected in synaptosomes from virgin samples, but less so in hyperosmotic or lactating animals. To investigate expression level changes of membrane proteins present in other cellular compartments, besides the nucleus, a P2+M fraction seems a viable choice.

SON dissection

Because the SON is located immediately adjacent to the optic chiasm it is easily identifiable in an acute slice preparation and can be dissected out using the punch technique [232,237].

However, a drawback of the punch dissection technique is that small pieces of neighboring hypothalamic nuclei or myelinated tracts from the optic chiasm may also be included in the sample, which will contaminate the SON protein content, and may do so with variable extent between samples. Even when mass spectrometry analysis is conducted optimally, variation in the input material will disrupt the extraction of biologically relevant regulations from the resulting dataset, and these regulation are unlikely to be reproduced on independent replicates.

Utilization of the punch technique also results in inclusion of material from all cell types.

Consequently, the heterogeneity of the sample means any cell-type specific protein regulation will be diluted and possibly become undetectable. The ability to sort specific cell-types from the tissue, possibly using fluorescence-or magnetic-activated cell sorting [193,194,238], would be beneficial towards identifying these specific changes. However, the low yield of such techniques combined with the high protein input requirement of the Orbitrap mass spectrometer rendered such a level of resolution unfeasible in the current experiment.

Sample preparation for mass spectrometry

Preparation of the samples for mass spectrometry involved running P2+M samples on SDS gradient gels and subsequently cutting each gel into 12 pieces. This was performed in order to reduce the overall complexity of each sample and to facilitate the detection of mass spectra from as many peptides as possible. Although every effort was made to standardize the way in which the gel was cut, minute differences in the duration of the electrophoresis or imprecisions while cutting would result in a slightly different subset of proteins being present in each slice. The effects of these deviations would result in shifts of protein populations during the HPLC elution and could consequently affect the mass spectrometer's ability to detect and quantify the corresponding peptides as only the top 5 most abundant peaks per scan were selected for MS-MS (i.e., via data dependent acquisition). Another consequence of the large number of slices is that samples had to be analyzed over a period of several weeks, in which fluctuations in the mass spectrometer's performance and sensitivity might have induced measurement aberrations.

Possibilities to improve the experimental design

Alterations can be made to the currently employed experimental protocol to reduce both technical and biological variation in order to achieve the desired astrocyte-specific plastic protein profile. In order to reduce biological variation, an ex-vivo model can be substituted for the lactating and hyperosmotic rats. The same structural plasticity observed in the SON during hyperosmosis and lactation can be induced in acute hypothalamic slices from pregnant rats with the joint application of oxytocin and hyperosmotic medium [236]. The ability to precisely time the application of the stimulus would allow for increased consistency in protein expression between samples. Additionally, one half of the slice could be used as a control (virgin) and the other half treated with the conditioned medium, so that each animal can act as its own control for even more accurate measurements of protein level changes.

Advances in mass spectrometry technology have also made it possible to now quantify a large number of proteins using a smaller amount of input. Using a more sensitive mass spectrometer than the one utilized in this study, it is possible to quantify more than 3000 proteins from only 5 µg of protein, compared with the 50 µg used here (Chapter 3). This very low input requirement means it will be possible to process the samples into sucrose synaptosomes, instead of a crude membrane fraction, to obtain a more precise overview of protein level changes specifically at the synapse. Furthermore, the in-gel digestion procedure requires the gel to be cut into only 2 slices thereby dramatically diminishing technical variation. The decreased number of slices also means the entire analysis time is limited so there is less opportunity for fluctuations in mass spectrometer performance to occur during the data acquisition.

Combining the reduced biological variation of precisely timed acute slices with cutting edge, high-sensitivity mass spectrometry can overcome the hurdles faced here to further our knowledge of the underlying mechanisms of astrocyte plasticity in the SON. The results of an improved mass spectrometry screen could confirm the up-regulation of Ndrg2 by SON stimulation, as well as identify additional target proteins. Interference with expression of Ndrg2 may impair astrocyte retraction and subsequently induce deficits in synaptic transmission in the SON, thus confirming its role in SON functional plasticity.

EXPERIMENTAL PROCEDURES

Induction of hyperosmotic and lactating conditions

Virgin female random-cycling Wistar Han rats (Charles River, the Netherlands) and pregnant Wistar Han rats (+/-E15, Harlan, the Netherlands) were used. All rats were 3-4 months of age.

Animals were housed on a 12 hour light/dark cycle and given access to standard lab chow and water ad libidum. All animals were sacrificed between 13.00h -17.00h. All experiments were conducted with approval of the Animal Users Care Committee of the VU University Amsterdam.

After 2 weeks of acclimation after arrival in the animal facility, rats in the hyperosmotic group (Charles River) had their water replaced with 2% NaCl water for 7-9 days. This protocol has previously been shown to induce hyperosmosis [217]. Pregnant rats were allowed to give birth and nurse litters for 12-15 days. Virgin rats were housed in the same room and left undisturbed.

Isolation of supraoptic nuclei from acute slices

Rats were decapitated by guillotine without anesthesia, as anesthetics have been shown to alter synapse physiology [239]. Brains were rapidly removed and chilled in an ice-slurry of artificial cerebrospinal fluid (aCSF, 125 NaCl, 3 KCl, 1.25 NaH2PO4, 1 MgSO4, 1 CaCl2, 10 glucose) for 1 min. The brain was roughly cut with a razor blade into a block containing the hypothalamus and then quickly submerged in a chilled vibrotome bath (Leica) with ice-cold carbogenated (95% O2, 5% CO2) aCSF. Using the optic chiasm as a guide, a 1 mm thick slice was cut which contained the SON. The slice was placed in a Petri dish with cold aCSF on ice and a punch of tissue, 1 mm in diameter containing the SON, was removed from each hemisphere [237]. The tissue punches were immediately frozen on dry ice.

P2+microsome (P2+M) isolation

Due to the small size of the supra optic nucleus, SON from 3 animals were pooled to generate each sample. Frozen (-80°C) SON were homogenized in ice-cold 0.32M sucrose/5 mM HEPES with added protease inhibitors (Roche). The homogenate was spun down at 1000x g for 10 min. to remove nuclei and cell debris. The resulting supernatant was then pelleted by ultracentrifugation at 30,000 RPM for 2 hours. The pellet (P2+M fraction) was resuspended in 25 mM HEPES and protein concentration of each sample was determined by Bradford assay (Biorad). Samples were stored at -80°C until further use. Ten P2+M samples were generated for each experimental condition, 4 to be used for mass spectrometry and 6 for immunoblot validations.

In-gel digestion

Four virgin, lactating, and hyperosmotic samples were analyzed by mass spectrometry. The protocol was performed as previously [199], with some modifications. For each sample, 50 ug of the P2+M sample was mixed with SDS sample buffer and denatured for 5 minutes at 98° C.

To block cysteine residues, 50 mM iodoacetamide was added to each sample and allowed to incubate for 30 minutes. Samples were run on 4-12% Bis-Tris NuPAGE gels (Thermo Fisher Scientific) in NuPAGE MOPS SDS buffer (Thermo Fisher Scientific). Gels were fixed overnight and stained with Coomassie blue to visualize all proteins. Each sample lane was cut into 12 equal sized pieces to reduce overall sample complexity. Each gel piece was further cut into ~1 mm 3 blocks and placed in a 96 well filter plate (Millipore). A collection plate (Eppendorf) was placed below the filter plate so that, after spinning plates for 1 minute at 200x g, waste solutions could easily be discarded. A fresh collection plate was used for the peptide elution phase. Destaining of the gel slices was achieved by two rounds of incubation in 50% acetonitrile/ 50 mM ammonium bicarbonate, dehydration by 100% acetonitrile, and rehydration in 50 mM ammonium bicarbonate. After the second acetonitrile dehydration gel pieces were rehydrated in 50 mM ammonium bicarbonate containing 10 ug/ mL of sequence grade trypsin (Promega) and incubated at 37 °C overnight. Peptides were extracted from the gel pieces with 3, 30 minute rounds of incubation in 50% acetonitrile/ 0.1% Trifluoroacetic acid. The extracted peptide solution was dried in a SpeedVac and stored at -20°C until mass spectrometry analysis.

HPLC-Orbitrap MS/MS

Peptides were re-dissolved in 0.1% acetic acid and injected into an Eksigent HPLC system coupled to an LTQ-Orbitrap mass spectrometer (Thermo Electron, San Jose, CA, USA).

Samples were initially trapped on a 5 mm Pepmax 100 C18 column (Dionex, 100 µm ID, 5 µm particle size) and then analyzed on a 200 mm Alltima C18 homemade column (100 µm ID, 3 µm particle size). Separation was achieved using a mobile phase from 5% acetonitrile, 94.9% H2O, 0.1% acetic acid (Phase A) and 95% acetonitrile, 4.9% H20, 0.1 acetic acid (phase B), and a linear gradient from 5% to 40% buffer B for 40 min. at a flow rate of 400 nL/min. The LTQ Orbitrap mass spectrometer was operated in data dependent mode, in which one full-scan survey MS experiment (m/z range 330-2000) was followed by MS-MS experiments on the 5 most abundant ions.

Peptide matching and quantitation

MaxQuant software (version 1.3.0.2) was used for spectrum annotation and relative protein quantification [201]. Spectra were annotated against the Uniprot rat reference proteome database (version 11-2012). Enzyme specificity was set to Trypsin/ P (normal trypsin cut, disallows cutting when the site is before a proline residue), while allowing for at most two missed cleavages. Carbamidomethylcysteine was set as a fixed modification, and Nacetylation and methionine oxidation were set as variable modifications. False-discovery rate cutoffs for peptide and protein identifications were 5%. Mass deviation tolerance was set to 20 ppm for mono-isotopic precursor ions and 0.5 Da for MS-MS peaks. The minimum peptide length was seven amino acids. When a protein was identified that has the same set of peptides or a subset of peptides compared with another protein, these proteins were merged into a protein group. Only proteins or protein groups are reported when at least a single unique peptide has been observed. Peptides that are shared between different proteins or protein groups that have unique peptide evidence, are assigned to the protein/protein group that has most peptide evidence. Label-free protein quantification was performed with

MaxQuant LFQ intensity values. To minimize noise, LFQ intensities were only calculated when samples had at least two identical and unmodified peptides that could be used for ratio calculation.

Mass spectrometry data analysis

Before statistical evaluation, proteins were filtered according to the times a MaxQuant LFQ intensity value was available for respective proteins; only proteins present in at least 2 samples of a single experimental group were included. To evaluate whether significant changes in protein expression occurred between experimental conditions, LFQ intensity values were subjected to Significance Analysis of Microarrays (SAM) analysis to derive permutation-based false discovery rates (FDR) [240]. Statistical analysis was performed in both multiclass (virgin, lactating, hyperosmotic) and pair-wise (virgin vs. lactating and virgin vs. hyperosmotic) designs.

Hierarchical clustering

Hierarchical clustering was performed on a subset of proteins that adhere to 22% FDR of differential protein expression.

For clustering of samples, protein LFQ intensity values were centered, and clustering was performed using a Pearson correlation coefficient-derived distance and the average-linkage method of agglomeration.

For clustering of proteins, protein LFQ intensity values were centered/scaled, and clustering was performed using Euclidean distance and Ward's method of agglomeration. The sixteen protein plasticity clusters were arbitrarily defined by cutting the protein-relationship dendrogram at a height that resulted in appropriate aggregation of related protein expression profiles.

Gene ontology analysis

For the gene ontology analysis the Bingo plug-in (v2.44) for Cytoscape (v2.8. In this study we aimed to quantitatively identify proteins that show altered hippocampal synaptic membrane expression over time, induced by a CS-US association compared with those regulated by the US alone during the period of contextual fear memory consolidation.

Specifically, we assessed biochemically enriched synaptic membrane fractions and performed proteomic analyses using an isobaric tag for relative and absolute quantification (iTRAQ) followed by tandem mass spectrometry and independent validation by immunoblotting. This approach identified proteins of which expression levels were altered over time due to associative learning, as well as changes resulting from only the aversive, stressful attributes of contextual fear conditioning.

RESULTS

Mice were conditioned using a standard contextual fear conditioning paradigm and received a single foot shock (0.7 mA, 2 s) after a delay of 180 s (delayed-shock group). As a control, we used the immediate shock deficit paradigm, in which mice receive the shock immediately on placement in the conditioning chamber (immediate-shock group), because they do not form an aversive association between the context and the shock (Fig. S2 in the Supporting Information) [181]. The dorsal hippocampus synaptic membrane proteome of animals that received either a delayed or an immediate shock during training was then analyzed 1 h and 4

h after conditioning (Fig. S2 in the Supporting Information).

We included proteins with 2 or more quantifiable peptides in all 6 samples of the three 8-plex replicates. Using these strict selection criteria we identified a total of 724 proteins (Table S2 in the Supporting Information). We next categorized these into 19 functions groups as described previously [183,196] (Fig. S2 in the Supporting Information).

Using this experimental set-up, we first analyzed changes in protein expression caused by the association of a foot-shock to the context by comparing the proteome of delayed shock animals to immediate shock controls 1 h and 4 h after conditioning.

Changes in the hippocampal synaptic membrane proteome composition 1 h and 4 h after fear conditioning

We first examined significant changes in protein expression at fixed time points (1 h and 4 h) (Fig. 1A). In order to discern changes in protein expression specific to associative learning, the immediate shock group was used as a control, as these animals receive a foot shock but do not form a fear memory to the context (Fig. S2 in the Supporting Information) [181,243]. SAM analysis revealed no significant changes in protein expression between the delayed and immediate shock groups 1 h after fear conditioning (DS1h-IS1h) (Table S2). However 4 h after conditioning 423 proteins were significantly regulated in the delayed shock group when compared with the immediate shock group, with 250 proteins up regulated and 173 down regulated (Fig. 1B,C, Table S2 in the Supporting Information; FDR q-value<5%). Of the 19 functional groups, the 5 groups with most regulation were 'Cell adhesion/Transsynaptic signaling' (77%), 'Endocytosis' (92%), 'Ion balance/Transport' (80%), 'LGIC signaling' (80%), and 'Tyrosine kinase signaling' (89%) (Fig. 1C, Fig. S3 in the Supporting Information). Examples of these proteins and their regulation values are shown (Fig. 1D).

To confirm the iTRAQ results for significant protein-regulation, immunoblotting was performed on an independent set of samples (Table S3 in the Supporting Information, Fig. 2).

To this end, following a conditioning session with either an immediate or a delayed shock, the level of expression of a representative set of 27 proteins belonging to different functional classes was measured (Table S3 in the Supporting Information). From these 27 proteins, 11

proteins showed significant regulation similar to that observed in iTRAQ, 10 proteins showed a trend of regulation in the same direction as iTRAQ, 3 proteins showed no regulation, and 3

showed a trend for regulation in the opposite direction of that of iTRAQ. Regulation measured by immunoblotting and iTRAQ showed a significant correlation (r 2 =0.439; P=0.022); Fig. S4 in the Supporting Information). Together, for 21 out of 27 proteins we confirmed the observed regulation. Since no significant changes were observed 1 h after conditioning, indicative of unchanged protein expression at this time point (Table S2, Fig. 1B), we hypothesized that the changes observed at the 4 h time point could be attributed to either 1) temporal changes in protein expression in the delayed shock group that would underlie associative learning, or 2) temporal changes in protein expression in the immediate shock group that cannot be attributed to learning, but to the delivery of the foot-shock alone. Indeed, we found that from the 423 proteins regulated at 4 h between the delayed and immediate shock groups, a substantial number of proteins were also temporally regulated within each group over the time-course of 4 hours (286 proteins, 67%; Table S2, Fig. 1B). Thus, we next focused on significant differences in the hippocampal synaptic membrane protein composition over the time course of 4 hours induced by either the delayed shock (DS4h-DS1h), the immediate shock (IS4h-IS1h),

or by both delayed and immediate shock for proteins with an opposite regulation.

Temporal changes in hippocampal synaptic membrane protein composition induced by delayed shock

The hippocampal synaptic membrane proteome was analyzed 1 h and 4 h after conditioning with a delayed shock (Fig. 3A). FDR analysis revealed that treatment of mice with a delayed shock resulted in a total of 164 proteins being significantly regulated over time (4 h vs. 1 h) (Table S2). There were 87 up regulated and 77 down regulated proteins (Table S2) distributed over 15 functional groups (Fig. 3B). The 5 groups with most regulation were 'Endocytosis' (36%), 'G-protein relay' (27%), 'Mitochondrion' (42%), 'Neurotransmitter metabolism/transport' (30%), and 'Protein synthesis/folding/breakdown' (27%) (Fig. S3 in the Supporting Information, Fig. 3B). Examples of these proteins are shown in Fig. 3C. Because protein expression is normalized to the average iTRAQ label peak area in the MS analysis, large abundance differences due to the regulation of mitochondrial proteins might influence the relative levels of all other proteins. Therefore, we reanalyzed the data with mitochondrial proteins excluded to determine a possible shift in regulations. However, similar regulations and P-values were observed (Regulations, r 2 =0.9983; P-values, r 2 =0.9913). Thus, the mitochondrial protein group did not by itself influence the overall protein normalization of the samples, and thereby the expression values measured. Interestingly, whereas many mitochondrial proteins were down regulated in the DS4h-DS1h comparison, a distinct set of mitochondrial proteins was down regulated in the immediate shock comparison (IS4h-IS1h; Of the 164 proteins significantly regulated over time after conditioning with a delayed shock, 53 were regulated by this condition alone, and 51 were regulated by this condition and between DS4h-IS4h (Fig. 1B, Table S2 in the Supporting Information). This indicates that whereas a number of proteins were regulated by learning alone, the stress of an aversive stimulation greatly influenced protein expression after fear conditioning. From these, a total of 61 proteins, i.e., 43 mitochondrial proteins (Fig. S3 in the Supporting Information) and 18 from other classes (Fig. 3D), showed a large difference in regulation when compared with the IS4h-IS1h dataset, indicative of opposite regulation by learning in the presence of a stressor (DS) and by a stressor in the absence of learning (IS).

Temporal changes in hippocampal synaptic membrane protein composition induced by immediate shock

We next investigated temporal changes in protein expression using the immediate shock deficit paradigm (Fig. 4A; Fig. S2 in the Supporting Information) [181]. FDR analysis revealed that treatment of mice with an immediate shock resulted in a total of 273 proteins being significantly regulated over time (4 h vs. 1 h) (Table S2 in the Supporting Information). There were 92 up regulated and 181 down regulated proteins distributed over 19 functional groups (Fig. 4B). The 5 groups with most regulation were 'Cell adhesion/Transsynaptic signaling' (64%), 'Cell metabolism' (55%), 'Endocytosis' (68%), 'G-protein relay' (53%), and 'Ion balance/Transport' (57%) (Fig. S3 in the Supporting Information, Fig. 4B), of which examples of regulated proteins are shown (Fig. 4C).

Of the 273 proteins significantly regulated over time after conditioning with an immediate shock, 28 were regulated by this condition alone, and 185 were regulated by this condition and between S2 in the Supporting Information). From these, a total of 125 proteins, i.e., 22 mitochondrial proteins (Fig. S3 in the Supporting Information) and 103 from other classes, showed a large difference in regulation when compared with the DS4h-DS1h dataset, again pointing to opposite regulation by the effects of a stressor in the absence of learning vs. learning in the presence of a stressor. The 103 proteins belonged to different classes, of which proteins from 'LGIC signaling', 'G-protein relay', 'GPCR signaling', 'Exocytosis', and 'Excitability are shown (Fig. 4D)

Differential changes in hippocampal synaptic membrane protein composition induced by immediate shock stress and by delayed shock-aversive learning

Besides proteins that were found to be regulated over time by either delayed or immediate shock, we found a total of 60 proteins that were regulated in both conditions, of which 50

were shared in the DS4h-IS4h comparison (Fig. 1B, Table S2 in the Supporting Information).

From these 50, 48 had opposite regulation in the IS4h-IS1h comparison, indicative of a large interaction effect of stress and learning. These proteins were represented by 12 functional groups. From these, 2 groups that stood out were 'Cell metabolism' (21%), and 'Endocytosis' Next, to determine whether changes were specific to either delayed or immediate shock, we made immunoblot comparisons to an additional no shock control group for specific protein groups that showed opposite regulation in the DS4h-DS1h vs. the IS4h-IS1h comparison.

These entailed proteins of the endocytosis, exocytosis, glutamate receptor subunits (LGIC signaling), neurotransmitter metabolism and protein clustering groups (Table S4 in the Supporting Information). Animals from the no shock group explored the context, but did not receive any aversive stimulation (Fig. 5A) and thus did not form a fear memory. As expected, for several proteins the regulation at 4 h could be attributed to protein regulation in the delayed shock group (e.g. GluN2a/Grin2a, Dlg3, Synj1), or in the immediate shock group (e.g., Synj1, Amph, Pacsin1) (Fig. 5B). However, we found that for other proteins, expression changes were due to regulation that were more pronounced by comparing with a no shock control. In the delayed group, these were GluA1/Gria1, GluN2a/Grin2a, GluN2b/Grin2b, Slc1a3, and in the immediate group Synj1, Pacsin1 showed more pronounced regulation compared with the no shock control. Together, the large regulation observed at 4 h between the delayed and immediate shock groups could be attributed to the presentation of an aversive stimulus during conditioning.

DISCUSSION

Here we characterized the dorsal hippocampal synaptic membrane proteome after contextual fear conditioning using a proteomics approach that allowed us to identify changes in protein levels that take place up to 4 h after conditioning induced by both delayed and immediate shock protocols. We identified a large number of proteins that were altered over the time course of memory consolidation of the aversive learning. However, presentation of a shock alone results in an even bigger change in the proteomic profile with time.

We focused this study on identifying protein expression changes after either a delayed or an immediate shock, only the former of which results in the formation of an aversive associative memory [181,243,259]. Both these conditioning groups were taken along for comparison in order to monitor changes that were caused by learning (delayed foot shock), the stress of an aversive stimulation (immediate foot shock) and the interaction of the two in the consolidation of an aversive associative memory. The two time points chosen were based on the observation that the type of conditioning protocol we used results in two waves of protein synthesis required for consolidation, one around the time of conditioning (at 1 h) and one about 4 h later [252]. It is important to note that only a small percentage of synapses and cells within the hippocampus actively encode the memory [260]. Since this proteomic study was done on the entire dorsal hippocampus the significant changes in protein expression we observe could be due to 1) changes at synapses that are activated specifically by learning and 2) global changes at the cellular and network level in non-learning synapses of activated cells and cells that are not learning-specific. These comprehensive changes are also important as they might contribute to global protein-synthesis mechanisms in the hippocampus that establishes and regulates a state that can allow synaptic changes and plasticity in a smaller subset of synapses that consolidate a long-term memory [261].

Using this experimental setup, it was possible to make four comparisons, namely 1) DS1h-IS1h, 2) DS4h-IS4h, 3) DS4h-DS1h, 4) IS4h-IS1h. The first two of these comparisons yield information on protein changes at a static time-point caused due to the association of a footshock to the conditioning context (delayed shock) versus the shock only (immediate shock), while the latter two comparisons result in a temporal profile of protein expression changes due to either learning (DS4h-DS1h), foot shock stress (IS4h-IS1h) or a combination of the two.

With regard to the comparisons at a static time point, we found no differences between the delayed and immediate shock groups 1 h after conditioning. This could be attributed to the fact that we study synaptic membrane fractions and any changes in protein expression at this time-point may not yet be observed at the synapses. Alternatively, the detection threshold using an open screening might be too low to detect subtle changes, or changes in a few proteins. Using the current method, 1 h post conditioning synaptic protein expression appears to be unchanged irrespective of whether an animal learns to associate an aversive stimulus to a context or not. Despite this apparent absence of regulation, it could well be that there is no differentiation in the synaptic response between learning and stress that could be different from a naïve state. This is exemplified by the overall down regulation of GluA1/Gria1 in the DS and the IS samples at 4 h compared with the no shock control (Fig. 5), whereas over time there was a small and non-significant change in both groups (DS4h-DS1h, IS4h-IS1h; Table S2 in Supplemental Information). On the other hand, we found very large differences between the hippocampal synaptic membrane proteome of these groups 4 h after conditioning. The proteins with the most regulation were found to be involved in a synaptic transmission processes, such as cell adhesion, LGIC signaling, endocytosis, ion balance and transport and tyrosine kinase signaling, that are critical for synaptic plasticity that can support learning.

Some of the proteins regulated at 4 h have previously been implicated in learning, such as the glutamate receptors [262] (GluN1/Grin1, GluN2a/Grin2a, GluN2b/Grin2b, GluA1/Gria1, GluA2/Gria2, GluA3/Gria3) and proteins belonging to the endocytotic machinery (eg, Dyn1, Amph, Synj1). Together these proteins could sculpt circuit responsiveness to favor learning by a small subset of activated synapses, while maintaining a constant level of synaptic drive.

This could occur by endocytosis of glutamate receptors thereby weakening synapses that are not activated, while synaptically strengthening those that participate in the memory.

Interestingly, a number of proteins belonging to the group of cell adhesion (e.g., Mbp, Bcan, Tnr, Cspg4, Gmp6a and Gmp6b) were down-regulated at 4 h. These proteins mediate structural stability by interacting with extracellular matrix proteins that influence synaptic plasticity and enshroud synaptic contacts involved in long-term memory storage. A decrease in expression of these proteins could allow for enhanced activity-dependent reorganization of the dendritic cytoskeleton to increase synaptic efficiency that guides learning. Although very interesting, the protein regulation observed at 4 h could be due to differences in protein expression in the delayed shock group or due to regulation in the immediate shock group as was observed by comparisons made to a no shock control (Fig. 5b). Furthermore, 67% of these changes in protein expression could be attributed to temporal changes in protein expression caused by either the immediate shock or the delayed shock (by stress or by learning). In order to tease out these differences, we next focused on significant differences in protein expression over time caused by either the delayed or the immediate shock, namely DS4h-DS1h and IS4h-IS1h, given that no differences were observed at the 1 h time-point.

After a delayed shock, 164 proteins were significantly regulated over time (DS4h-DS1h). There appeared to be an up regulation of a majority of proteins involved in endocytosis, G-protein relay and protein synthesis/folding/breakdown and a down regulation of mitochondrial proteins and those involved in neurotransmitter metabolism/transport. The direction of regulation of these proteins suggests that a majority of dorsal hippocampal synapses undergo a temporal decrease in synaptic efficacy and neurotransmission. It might be speculated that the changes we observe are more global, and are responsible for regulating and maintaining an optimal balance for plasticity events in a smaller subset of synapses that encode the memory. This is in line with the idea that within the hippocampus only a sparse population of neurons actively participates in the formation of a memory [253,260]. A large number of the remaining non-activated synapses and cells may partake in post-learning cellular-and network-level information sculpting through LTD-like mechanisms that reduce basal firing rates and increase signal-noise ratio, thereby enabling the activated neurons to select and shape learning-acquired synaptic information for long-term encoding [263]. Interestingly in this aspect, the down regulated proteins involved in neurotransmitter transport (EAA1/Slc1a3, EAA2/Slc1a2, Slc6a11) are typically astrocyte-enriched [191]. Astrocyte proteins have previously been detected in synaptic fractions [197,264] and astrocyte neurotransmitter transporters are well known to regulate glutamate and GABA concentrations in the synaptic cleft, and as such influence synaptic efficacy [106,130]. The down regulation of these astrocyte neurotransmitters transporters 4 hours after delayed shock will lead to reduced clearance of (extra-) synaptic glutamate and/or GABA. With a single rodent astrocyte associating with multiple neurons and contacting up to 100,000 synapses [START_REF] Bushong | Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains[END_REF]265], one may speculate that the observed down regulation of these astrocyte neurotransmitter transporters serves to balance a tone of neuronal activity across large hippocampal areas as part of the post-learning cellular-and network-level changes.

Furthermore, these data also indicate that the activation of second messenger systems may result in protein synthesis and structural modifications underlying the remodeling of synapses that is required for memory consolidation [247,255,266]. It is interesting to note that we see no temporal increase in glutamate receptor levels 4 h after conditioning, which is in contradiction to a previous study that showed that overexpressed GFP-GluA1 receptors were observed in the hippocampus starting from 2 h after conditioning and lasting for more than

h [253]

. There may be several reasons for this discrepancy, the main one being that this increase at early time points was observed for somatic receptors that were in large abundance due to overexpression, whereas our study focuses on expression changes at the synaptic membrane.

Somewhat surprisingly, we found that delivery of an immediate shock, which is a commonly used control for contextual fear memory experiments, also results in huge changes in the synaptic membrane proteome. A total of 273 proteins were significantly regulated, with most regulation being observed in proteins involved in cell adhesion/transsynaptic signaling, cell metabolism, endocytosis, G-protein relay and Ion balance/transport. This leads us to believe that shock alone causes large-scale changes in the hippocampal synapses, despite the fact that this does not lead to a fear memory of the event. Besides being an important brain locus for the processing of contextual memories, the hippocampus is vulnerable to stressful experiences [267] and is also involved in the processing of the shock [244]. In the immediate shock group, animals do not explore the environment prior to receiving the shock, and so perceive it different than with a delayed shock. In case of the latter, the context has been explored before delivery of the aversive stimulus resulting in the formation of an aversive association. This might result in the differential effect we observe on synaptic protein levels within the hippocampus, which in turn might affect the consolidation of memory and subsequent behavior. In line with this, pre-exposure to the conditioning context has been shown to alleviate the immediate-shock deficit in associative fear learning [244,245]. Stress has been shown to engage the same underlying mechanisms as LTP in the hippocampus, impairing and occluding subsequent synaptic potentiation that underlies learning [267,268].

Stress may also interfere with memory formation by affecting metaplasticity and thereby the optimal balance of synaptic plasticity within memory circuits [267][268][269]. At the molecular level, especially when comparing to a no shock control (Fig. 5b), we see an up regulation of GluA2 and a down regulation of GluA1 in animals that receive an immediate shock, suggestive of a shift in subunit composition of the AMPA receptor. It would be interesting to test whether reversing this regulation to that in the delayed shock group rescues the immediate shock deficit in these animals.

In total, 60 proteins were significantly regulated over time in both the delayed shock and immediate shock groups (Fig. 1b). These are of interest because the direction of regulation of the majority, i.e., 48, of these proteins is different between the two groups. An active memory trace is formed only in the presence of a delayed shock and not with the delivery of an immediate shock [181,259]. Thus, whereas the stress of foot shock is present during both conditioning protocols, it appears to have a deleterious effect on learning only when given immediately on placement in the conditioning chamber. Previous data have shown a role for learning in modifying the adverse effects of stress on the hippocampus. For instance, stressinduced impairments in synaptic efficacy are reduced when the stress is paired with learning [270]. However, the neural mechanisms that sub serve this effect of learning is poorly understood. From our results, it can be speculated that exploration of the context prior to the delivery of the foot shock results in a reversal of expression of a critical set of proteins disregulated by foot-shock stress and this might promote synaptic transmission efficacy that allows for an association between the context and shock to be consolidated. Indeed, multiple proteins-groups with differential regulation are found. These involved either cell metabolism or endocytosis, which apparently are associated with synaptic modification depending on the conditioning protocol used. For instance, Protein kinase C and casein kinase substrate in neurons protein 1 (Pacsin1, also named Syndapin1) show a strong up regulation after a delayed shock, while being strongly down regulated over time by the presentation of an immediate shock stimulus. This protein is involved in the clustering of the endocytotic machinery and the activity-dependent endocytosis of NMDARs [271,272], potentially conferring a differential spatiotemporal role for these receptors that is dependent on the conditioning protocol used. More specifically, a role for Pacsin1 on the presynaptic site is its involvement in vesicle recycling [273,274] In particular under high neuronal activity, synaptic vesicle retrieval occurs via a Dynamin-dependent way, in which a dephosphorylated form of Dynamin binds to Pacsin1. During learning, increased cellular activity might recruit this process, requiring an up regulation of Dynamin, which has a similar pattern of regulation in our paradigms as Pacsin1. Indeed, dynamin is upregulated when compared with the no shock control at 4 h after the delayed shock and down regulated after the immediate shock, as confirmed by immunoblotting (Fig. 5b). Thus, although the context and delivery of a shock (but not its temporal placement) are common features in our learning and stress paradigm, the association of a delayed foot shock with the conditioned stimulus appears to alter protein expression of this selected group that might underlie the difference in behavioral outcome observed (Fig. 1b).

Taken together, this proteomic study gives a new insight in the change of protein levels in the synaptic membrane proteome of the dorsal hippocampus after contextual fear conditioning.

These changes in glutamate receptor expression and scaffolding proteins as well as endocytosis proteins contribute to the remodeling of synapses and synaptic plasticity that is required for the processing of an aversive and stressful stimulus, and the association between this aversive stimulus and a context. This study shows that temporal placement of a foot shock during conditioning greatly influences temporal protein expression that can be critical to whether an aversive fear memory is consolidated or not.

EXPERIMENTAL PROCEDURES

The setup of the entire experiment has been depicted in Fig. S1 of the Supporting Information

Animals and fear conditioning

Animals -All experiments were carried out in accordance to the Animal User Care Committee of the VU University. Adult male C57BL/6J mice (20-25 g, Charles River) were individually housed at a 12-h light/dark cycle with ad libitum access to food and water. Experiments were performed during the light phase. All mice were 9-10 weeks of age during testing.

Contextual fear conditioning -All experiments were carried out as described previously [181] in a fear conditioning system (TSE-Systems, Bad Homburg, Germany). Training was performed in a Plexiglas chamber with a stainless steel grid floor with constant illumination (100-500 lx)

and background sound (white noise, 68 dB sound pressure level). The chamber was cleaned with 70% ethanol prior to each session. For the delayed shock groups, training consisted of placing mice in the chamber for a period of 180 s after which a 2-s foot shock (0.7 mA) was delivered through the grid floor. Mice were returned to their home cage 30 s after shock termination. For the immediate shock group a 0.7-mA, 2-s foot shock was delivered immediately on placement in the conditioning chamber, after which the mice were allowed to explore the context for 210 s (180 s + 30 s). For the no shock group, mice were allowed to explore the conditioning chamber for 210 s in the absence of any foot shock. Using these protocols, we have previously shown that a fear memory is consolidated on presentation of a delayed shock, whereas a memory deficit is observed in the immediate shock group and no shock group [181].

Preparation of synaptic membrane fractions

We dissected the dorsal half of the hippocampus at the desired time points (1 h and 4 h after conditioning) from fresh brains and stored them at -80 C. Synaptic membrane fractions were isolated (pooled hippocampus of three mice, n=6 biologically independent samples/group) on a discontinuous sucrose gradient, as described previously [181,275]. Protein concentration was measured by a Bradford assay (Biorad). For each sample, 75 μg of protein was transferred to a fresh tube and dried in a SpeedVac overnight.

Protein digestion and iTRAQ labeling

Two-dimensional liquid chromatography (2DLC) and MALDI-MS/MS

As described previously, lyophilized iTRAQ labeled samples were separated in two dimensions. First by a strong cation exchange column (2.1 x 150 mm polysulfoethyl A column, PolyLC), and subsequently on an analytical capillary reverse phase C18 column (150 mm x 100 µm i.d. column) at 400 nL/min using the LC-Packing Ultimate system. Following this, peptides were separated using a linear increase in concentration of acetonitrile from 4 to 72% in -cyanohydroxycinnaminic acid in 1 mL 50% acetronitrile, 0.1% trifluoroacetic acid, 10 mM ammonium dicitrate), delivered at a flow rate of 1.5 µL/min and robotically deposited onto an Applied Biosystems matrix-assisted laser desorption ionization metal plate for a total of 384 spots. The samples were then analyzed on an ABI 4800 proteomics analyzer (Applied Biosystems, Forster City, CA). Peptide collision-induced dissociation was performed by air at 2 kV. MS/MS spectra were collected from 2500 laser shots. Peptides with signal-to-noise ratios over 50 at the MS mode were selected for MS/MS analysis, at a maximum of 30 MS/MS per spot. The precursor mass window was set to a relative resolution of 200. Peak lists were extracted from the instrument database using TS2Mascot software (MatrixScience).

Protein identification

MSMS spectra were annotated against a concatenated target-decoy database of mouse sequences in the Uniprot/Swissprot database (version 07/2012) using Mascot Server software (version 2.3.01, MatrixScience). Database searches were performed with trypsin specificity allowing two missed cleavages. Modifications on lysine residues and N-termini, and methylthio-modifications on cysteine residues were set as fixed modifications, whereas iTRAQ-modifications on tyrosine residues, and oxidation of methionine residues were allowed as variable modifications. Mass tolerance was 200 ppm for precursor ions and 0.4 Da for fragment ions. For each spectrum the best scoring peptide sequence was used. Protein inference was performed using in-house modified IsoformResolver software aiming for consistent protein assignment of peptides across experiments [276]. False discovery rates (FDR) for peptide and protein identifications were established using Mayu software [277].

FDR of protein identification was based on 'unique' (assigned to a single globally inferred protein) spectra exclusively. Spectra and proteins included in downstream analysis were both truncated at 5%FDR.

Protein quantification and identification of differentially expressed proteins

For protein quantification only unique spectra within 5% peptide identification FDR were used. If multiple spectra were assigned to the same peptide sequence in an experiment, only the spectrum with the highest ions score was used for quantification. Spectra with very low iTRAQ reporter signals (maximum intensity of any of the reporter ions less than 100) were removed. Only proteins with a minimum of two unique peptides in each experiment passing the above criteria and within the 5% protein level FDR were considered for quantification.

After correction for isotope impurities, iTRAQ reporter ions were log2 S2.

Supplemental Table S2. Proteomics data set. The full proteomics data set is shown as an excel sheet with protein and gene names for 724 proteins, the functional group, and for each of the three iTRAQ sets the number of peptides, the average area of the iTRAQ peaks corresponding to the lab els, as well as the regulation, SD and SEM and FDR q-value for the different comparisons is shown.

Significant q-values (<0.05) are highlighted.

Table S2 is available by contacting the author

Supplemental Table S3. Validation DS4h-IS4h by immunoblot analysis. For different functional groups, the protein validated (protein and gene name), its regulation, SEM, and q-or p-value is indicated for the iTRAQ and immunoblot analysis, respectively. Significance (<0.05) and trend (<0.20) for q-or p-values are highlighted.

INTRODUCTION

Contextual fear condition is a well-established paradigm for the examination of hippocampusdependent spatial memory processes [242]. When mice are trained to associate an aversive event, such as an electrical foot shock, to an otherwise neutral environment, a cascade of modulatory events ensues to solidify this association into a memory, a process termed consolidation [254]. Successful fear memory consolidation manifests in freezing behavior when the mouse is returned to the environment in which the foot shock was delivered, which can be quantified and used a measure for memory retention [281].

Consolidation consists of several phases, which can vary depending on the type and strength of memory being formed, but in general consist of a first phase (0-6 hours) of synaptic consolidation, and a second phase of systems consolidation beginning 6 hours after acquisition of the memory [252]. The synaptic consolidation phase involves the activation of kinases, including protein kinase c (PKC) and calcium/calmodulin-dependent protein kinase II (CamKII), as a result of increased calcium after synaptic depolarization [282,283]. This leads to the activation of transcription factors, including cAMP-responsive-binding-element (CREB), and initiation of cAMP signaling cascades which result in gene transcription [284]. Proteinkinase A directly phosphorylates target proteins in the synapse, such as GluA1, but can also translocate to the nucleus to phosphorylate targets to increase gene expression [285,286].

Expression of immediate-early genes in response to induction of consolidation, such as activity-regulated cytoskeletal protein (Arc), can be seen in both neurons and astrocytes 2 hours after stimulation, suggesting astrocytes play a role in synaptic consolidation [287,288].

Additionally, the synapse undergoes pronounced structural changes. In as little as 30 minutes after synaptic potentiation, enlargement of spine heads and post-synaptic densities can be detected [289,290]. Interestingly, potentiation of synapses after either tetanic stimulation or oxygen-glucose deprivation also results in remodeling of astrocyte processes, with increased astrocytic coverage of both pre-and post-synaptic compartments of potentiated synapses [303,304]. Systems consolidation also involves homeostatic synaptic scaling, which occurs to regulate the overall level of excitatory and inhibitory inputs to each neuron in order to maintain relative signal strengths [305,306].

Intervention studies using different drugs and blockers at various time-points over the course of consolidation have revealed there is no standard recipe for consolidation, and molecular processes governing short-and long-term memory consolidation are partially independent [307,308]. The transition through the stages of memory encoding depend on a variety of factors, including intensity of the training protocol, whether or not there was a retrieval session, quality of sleep, and hormonal state [309][310][311][312].

However, factors regulating the consolidation of memory are not limited to mechanisms involving the neurons. Astrocytes in the hippocampus have also been proven to play critical roles in the modulation of synaptic plasticity and synaptic scaling [START_REF] Henneberger | Long-term potentiation depends on release of D-serine from astrocytes[END_REF][START_REF] Stellwagen | Synaptic scaling mediated by glial TNF-alpha[END_REF]313]. Deletion of the astrocytic protein aquaporin 4 (Aqp4) in mice results in impaired hippocampal long-term depression (LTD), impaired long-term potentiation (LTP) in the hippocampus and amygdala, and deficits in object-location recognition and light-induced fear conditioning memory [314,315]. These impairments were linked to a decreased expression of the astrocytic excitatory Interestingly, other astrocyte proteins have been reported to convey a negative effect to synaptic plasticity. When the glial calcium binding protein S100B is genetically ablated, mice

show enhanced LTP and accordingly outperform their wild type littermates in both the Morris water maze and contextual fear conditioning [317]. Mice lacking the astrocytic potassium inward rectifying channel Kir4.1 show increased levels of post-tetanic synaptic stimulation, likely because the astrocytes are unable to buffer the accumulated K + after tetanic bursting [143]. These examples illustrate that astrocytes are critical nervous system components required for the induction of memory consolidation, but also that they impose important inhibitory constraints on neuronal function to prevent over-excitation.

In most of the aforementioned studies, behavior was only assessed at a single time point during the consolidation of the memory. However, memory consolidation is a dynamic process and increases in synaptic strength can continue to occur days after training [318]. In order to gauge the possible involvement of astrocyte proteins over the course of consolidation we used a bioinformatics selection procedure to choose 15 top-candidate astrocyte proteins for their previous implications in plasticity processes and evaluated their protein expression level in a consolidation time series of 4, 7, 24, 48, and 72 hours after memory acquisition. Mice trained in a contextual fear conditioning paradigm learned to pair a foot shock with a specific context (delayed shock), or they received the foot shock but were not allowed to make the associate of the foot shock to the environment (immediate shock),

or they experienced the environment with no noxious stimuli (no shock). Astrocyte processes are known to directly contact and interact with neuronal synapses, thus forming tripartite synapses [START_REF] Araque | Tripartite synapses: glia, the unacknowledged partner[END_REF]. As such, preparations enriched for synaptic compartments, including synaptosomes and synaptic membranes, also contain perisynaptic astrocyte processes and can be used to quantify their abundance (Chapters 3 and 5). In this manner it was possible to distinguish astrocyte proteins regulated specifically by the consolidation process, and those that were regulated as a result of the stressful foot shock. Our results revealed dynamic patterns of astrocyte protein regulation during both early and late phases of consolidation.

One of these regulated proteins, MLC1, an astrocyte protein involved in water and ion homeostasis, was selected to evaluate a functional role in consolidation [319]. Behavioral analysis of Mlc1 null mice revealed impaired contextual fear memory, suggesting a role for this astrocyte protein in memory consolidation.

RESULTS

Identification of astrocyte proteins regulated during memory consolidation

To identify astrocyte proteins possibly involved in the consolidation of fear memory a list of potential proteins was generated, for which several data sources were combined to narrow down the most likely candidates. First, because we were exclusively interested in astrocyte proteins, a mandatory criteria for eventual target proteins was the presence on a manuallycurated list of 1998 astrocyte-enriched genes [191] (Figure 1 (Supplemental Table 1). A total of 1,840 unique targets were identified. To reduce the number of false-positives, only the 78 targets found in at least two of the included studies were used for our target candidate selection (Supplemental Table 2). Fourth, to select proteins that may be part of general synaptic plasticity mechanisms across the brain, a list of 985 proteins significantly regulated (FDR ≤ 22%) by lactation and hyperosmosis induced structural neuronglia plasticity in the supraoptic nucleus (SON) were also included (Chapter 4).

Commonalities between these 4 sets of potential plasticity-related proteins were evaluated by the generation of a Venn diagram (Figure 1). This resulted in a list of 212 candidate proteins, that were astrocyte-enriched, and part of at least one of the 3 sets involved in synaptic plasticity and/or memory related processes (Supplemental Table 3).

Proteins not regulated by fear memory consolidation

The glycolytic enzyme Aldoc, the metabolic enzyme Glo1, the multi-functional enzyme alphaenolase Eno1, and the glutamate transporter GLT-1 were not regulated at 24 hours in any experimental fear conditioning condition (Figure 2 A-D). These proteins were further evaluated at 4 hours and 7 hours after training but did not demonstrate any significant expression level regulations, indicating that modulation of protein levels for these 4

candidates is not critical to the consolidation of fear memory.

Proteins regulated during both synaptic (4 h) and systems (7, 24, 48, and/or 72 h) level fear memory consolidation

Four proteins were found to be significant regulated during synaptic consolidation at the 4 hour and later time points, namely Atp1a2, Ndrg2, GFAP, and Ezr.

Atp1a2, a known perisynaptic astrocyte process (PAP) protein, is a transmembrane protein responsible for maintaining Na + /K + gradients [334] and mice deficient in Atp1a2 have increased anxiety levels and show exaggerated freezing responses 24 hours after contextual fear conditioning [335]. In the consolidation time-series, Atp1a2 was significantly regulated a 4, 7, and 24 hours after training (Figure 4A). At all evaluated time points, a significantly decreased expression level was seen in the DS compared with NS (-1.21-fold at 4 h, -2.38fold at 7 h, and -1.68-fold at 24 h), as well as a decrease compared with IS at 4 hours (-2.15fold) and 7 hours (-2.10-fold), indicating the regulations at 4 and 7 hours are specific to the fear memory. At 24 hours, Atp1a2 expression in the IS group fell to match that of the DS group (-1.67-fold compared with NS), suggesting protein regulation at this point may be due to stress effects of the shock itself. Blots for 48 and 72 hours were unsuccessful leaving regulations at later time points to be determined.

Ndrg2 is an astrocyte-specific protein expressed in the cell body as well as in distal processes [224]. Ndrg2 regulation at 4 hours was specific to fear memory consolidation with a decreased expression in the DS group compared with both NS (-1.18-fold) and IS (-1.18-fold) animals (Figure 4B). Although at 7 hours after training Ndrg2 levels had equalized, a second phase of highly significant regulations followed at 24 hours. Dramatic decreases in protein expression were detected for both IS (-3.21-fold) and DS (-3.34-fold) compared with NS animals.

Expression levels in the DS group shifted again at the 48 hour time-point with an increased expression compared to both NS (1.25-fold) and IS (1.26-fold) groups, indicating a consolidation specific effect on Nrdg2 even at this late time point. Immunoblotting for 72 hours was unsuccessful.

GFAP is an intermediate filament abundantly expressed in astrocytes and used as the classical astrocytic marker [336]. Deletion of this protein results in altered synaptic function including impaired LTD and enhanced LTP, suggesting a role for GFAP in the modulation of synaptic strength [337,338]. In accordance with these previous findings, significant GFAP regulation was found during the synaptic consolidation window at 4 hours after training (Figure 4C). GFAP levels were specifically regulated by memory consolidation with increased DS expression compared with IS (1.60-fold) and a strong trend towards a significant increase compared with NS (1.37-fold, p = 0.09). Similar to the expression pattern of Ndrg2, no change was found in GFAP levels at 7 hours but a large alteration in expression levels was present at 24 hours after training when GFAP levels decreased in both IS (-3.72-fold) and DS (-4.05-fold) conditions. Trends toward significant regulation were observed for GFAP at both 48 hours (F; p = 0.06) and 72 hours (F; p = 0.11). At 48 hours, DS expression was elevated compared to IS (1.33-fold) and at 72 hours expression decreased relative to NS animals for both IS (-1.54fold) and DS (-1.46-fold).

Ezrin is a cytoskeletal-adaptor protein critical to the motility of perisynaptic astrocyte processes [137,138]. Significant effects on protein expression were found at 4 hours and 72 hours after training (Figure 4D). At 4 hours ezrin levels were decreased in DS compared with NS (-2.3-fold), although not compared with IS. Because the ezrin levels in the IS group were lower than in NS, both stress effects of the shock and synaptic consolidation (Legend continued from previous page) C) GFAP: 4 h: F(2, 12) = 4.09, p = 0.04, DS vs. IS was 1.60fold, p = 0.02 , DS vs. NS was a trend of 1.37-fold, p = 0.07 ; 24 h : (F(2,12) = 14.08, p = 0.001, IS vs. NS was -3.72-fold, p = 0.001, DS vs. NS was -4.05-fold, p <0.001; A trend was observed at 48 h: F(2,11) = 3.65, p = 0.06, DS vs. IS was 1.33-fold, p = 0.02 ; A trend was observed at 72 h: F (2,12) = 4.45, p = 0.11, IS vs. NS was -1.54-fold , p = 0.05, DS vs. NS was (-1.46-fold, p = 0.10. D) Ezr: 4 h: F(2,12)= 4.67, p = 0.03, DS vs. NS was -2.3-fold, p = 0.01 ; a trend was observed at 7 h: F(2,12)= 3.05, p = 0.09, IS vs. NS was -1.78-fold, p = 0.04, DS vs. NS was -1.64-fold , p = 0.10 ; 72 h: F(2,12)= 29.72, p <0.001, IS vs. NS was 2.3-fold, p = 0.01, a trend for IS vs. DS was 1.9fold, p = 0.06. Bar graphs represent mean + SEM. * = p ≤ 0.05, ** = p ≤ 0.005, *** = p ≤ 0.0005. may slightly lower ezrin expression so that only the combination of stress effects and contextual learning in the DS result in a significant regulation compared to NS animals. A similar expression pattern persisted at 7 hours with borderline significance (F; p = 0.09). A surprising shift in ezrin expression was observed at 72 h, when increased levels were seen specifically in the IS condition compared with NS (2.3-fold) as well as with a strong trend compared with DS (1.9-fold).

Proteins regulated only during systems consolidation (7, 24, 48, and/or 72 h)

GAT3 is expressed by glial cells throughout the brain and is responsible for clearance of excess GABA from the extracellular space and can thereby modulate GABA-induced inhibitory postsynaptic currents and basal tone [339][340][341][342]. Significant effects were found only at the 24 hour time-point (Figure 5A). GAT3 expression was decreased in both IS (-1.18 -fold) and DS (-1.53 -fold) as compared with NS animals, indicating this regulation may be the result of shockrelated stress.

Echs1 is a mitochondrial protein that catalyzes fatty acid beta oxidation [343]. Increased Echs1 expression has been associated with a variety of tumors [344,345], but little is known about the specific roles of Echs1 in the brain. Whereas protein levels were unaffected during early consolidation time-points, a trend towards increased expression by DS (p = 0.10) at 4 hours was observed. However, large and highly significant expression changes were detected at both 24 and 72 hours after training (Figure 5B). At 24 hours, Echs1 expression was decreased in both IS (-3.72-fold) and DS (-3.60-fold) groups as compared with NS. This considerable down-regulation persisted at 72 hours after training in both IS (3.06-fold) and DS groups (-4.35-fold), implying a long-lasting effect of the shock-induced stress.

GS is an astrocyte-specific enzyme of the glutamate-glutamine metabolic cycle and is expressed in both the cell body and fine PAPs [346][347][348]. As neurons require glutamine supplies from astrocytes to generate glutamate for synaptic transmission, inhibition of glutamine synthetase in astrocytes results in memory impairments [349]. Despite the known involvement of GS in memory consolidation, absolute levels of GS were unaltered during the protein synthesis-dependent phase of memory consolidation (Figure 5C). However, significant regulations were seen at 24 and 72 hours after training. The effects at 24 h appear stress-related with comparable decreases in both IS (-1.29-fold) and DS (-1.33-fold) conditions. At 72 hours a sizeable increase in GS expression in both IS (10.19-fold) and DS (3.29-fold) were detected. Interestingly, the increased protein expression in the IS group was also significantly larger than that of the DS group (3.10-fold), perhaps indicating the shock alone induces large GS up-regulations at 72 hour, whereas the consolidation process reduces GS levels at that time.

Bsg is a monocarboxylate transporter chaperone protein involved in the regulation of lactate metabolic communication between neurons and astrocytes [350,351]. It promotes the outgrowth of astrocytic processes in vitro [195], and mice lacking Bsg show short-term memory deficits in the Y-maze and spatial memory deficits in a water-finding task, suggesting roles in both long and short term memory processing [352]. Compared with the IS, DS mice showed a trend (p-0.11) towards decreased Bsg expression at 4 hours. Significant regulations were found at 24 and 72 hours after training (Figure 5D). Bsg expression at 24 hours increased in both IS (1.23-fold) and DS (1.26-fold) conditions. Similar to the GS expression pattern at 72 hours, substantial Bsg increased expression occurred in both IS (10.9-fold) and DS (6.2-fold) groups.

GLAST is an astrocyte specific transporter expressed primarily in the cerebellum, but also in other brain regions, including hippocampus, and is present on PAPs [334]. It serves critical functions in brain homeostasis by clearing excess glutamate from active synapses and preventing excitotoxicity [353,354], and GLAST knock-out mice show impairments in discrimination-based learning [175,355,356]. Our consolidation time-series detected a trend (F; p = 0.12) towards decreased GLAST expression in DS at 4 hours, whereas significant changes were found at 7, 24, and 48 hours after training (Figure 5E). At 7 h, GLAST expression was slightly up-regulated in the IS group compared with both NS (1.18-fold) and DS (1.18fold), suggesting stress alone increased GLAST expression at this point, whereas the consolidation process lowers it. By 24 hours after training, GLAST expression had significantly decreased in both IS (-1.64-fold) and DS (-1.61-fold) as compared with NS animals. The significant down-regulation in the IS group compared with NS persisted at 48 hours (-1.68fold), and was also now down-regulated compared with DS (-1.53-fold). GLAST levels returned to NS control levels in the DS group, again suggesting opposite directions of protein expression by the stress effects and consolidation process. (Legend continued from previous page) D) Bsg: 24 h: F(2,12) = 6.53, p = 0.01, IS vs. NS was 1.23fold, p = 0.01, DS vs. NS was 1.26-fold, p = 0.007 ; 72 h: F(2,12) = 29.72, p <0.001, IS vs. NS was 10.9-fold, p <0.001, DS vs. NS was 6.2-fold, p <0.001. E) Glast: 7 h: F(2,12) = 3.92, p = 0.05, IS vs. NS was 1.18-fold p = 0.03, IS vs. DS was 1.18-fold IS, p = 0.03; 24 h: F(2,10) = 17.42, p = 0.001, IS vs. NS was -1.64-fold, p <0.001, DS vs. NS was -1.61-fold, p = 0.001 ; 48 h: F (2,11) = 5.86, p = 0.02, IS vs. NS was -1.68-fold, p = 0.01, IS vs. DS was -1.53-fold, p = 0.02. F) Mlc1: 24: F(2,12) = 5.80, p = 0.02, IS vs. NS was -1.78-fold, p = 0.005, DS vs. NS showed a trend of -1.41-fold, p = 0.11. Bar graphs represent mean + SEM. * = p ≤ 0.05, ** = p ≤ 0.005, *** = p ≤ 0.0005.

MLC1 is a transmembrane protein expressed in astrocytes involved in water and ion homeostasis [319,357]. Due to limitations in antibody availability, significant regulations for MLC1 were only assessed at 24 hours after training (Figure 5F). Expression was significantly decreased in the IS group (-1.78-fold) with a trend towards reduction in the DS group (-1.41fold). Although MLC1 is generally localized to the astrocytic endfeet comprising the bloodbrain barrier or at cell-cell junctions, it has been shown to form molecular complexes with known PAP proteins Aqp4, Kir4.1, and Atp1a2, suggesting possible additional roles at the synapse [START_REF] Nagelhus | Aquaporin-4 in the central nervous system: cellular and subcellular distribution and coexpression with KIR4.1[END_REF]334,357,358].

Trends in protein regulation over time

To evaluate trends in global protein changes at each time point, log2 expression ratios for NS against DS and IS were plotted for all measured proteins, regardless of significance, and represented as a single dot per protein, per time-point (Figure 6). A total of 14 proteins were evaluated at 4 hours, 14 at 7 hours, 15 at 24 hours, 8 at 48 hours, and 6 at 72 hours. At 4 hours after training, several proteins showed regulation mainly along the DS-NS axis indicating that these proteins are primarily regulated by the DS and are thus specific to fearmemory consolidation. A few proteins were also regulated in opposite directions by DS compared with IS, implying the activation of different pathways for the initial process of memory consolidation vs. foot shock processing. At 7 hours, protein expression had equalized All proteins evaluated by immunoblot were included, regardless of whether significant regulations were detected. At 4 h, the 4 dots most decreased by DS represent Ppap2b, GLAST,Atp1a2,and Ezr. between IS and DS for most proteins, thus large-scale protein regulations at this time point may not be critical to consolidation of the aversive fear memory or to the stress response to the foot shock. In contrast, many large down-regulations in protein expression were seen at 24 hours. Regulations at this point are generally in the same direction and to equal extents in both IS and DS conditions, indicating activity shock-related protein synthesis. A similar pattern persisted at 48 hours, although the magnitude of these changes was reduced. Remarkably, at 72 hours the IS induced sizeable up-and down-regulations in protein expression. Most proteins were regulated in the same direction by the DS, but to a lesser extent, demonstrating that molecular processing of the fear memory and shock-stimulus persisted for at least 3 days after training. Overall these data indicate that consolidation-specific astrocyte protein regulations occur primarily during the window of synaptic consolidation whereas regulations from 7-72 hours are largely the result of foot-shock stress.

MLC1 null mice show deficits in contextual fear memory

To further explore the potential roles of astrocytes in fear memory consolidation, one of the regulated proteins, MLC1, was chosen for further evaluation of its role in spatial memory consolidation. MLC1 null mice and wild type (WT) littermates were tested in contextual fear conditioning. Mice were placed in a fear conditioning chamber (Context A) and allowed to explore for 180 seconds before delivery of a single 0.7 mA foot shock. Retention of the fear memory was assessed by returning mice to the conditioning box 24 hours after training and measuring freezing behavior. During the 180 seconds of context exploration prior to the foot shock, baseline behavioral parameters were assessed. MLC1 null mice did not differ from WT control in percent of the cage explored, percent inactivity, or total distance moved, together indicating no gross motor abnormalities (Figure 7A). These baseline levels of behavior were comparable to those of other WT mice previous assessed in this paradigm [359]. Both groups of mice also had equally low (<10%) freezing levels prior to the shock, indication no preexisting fear for the conditioning box. Assessment of freezing behavior 24 hours after the foot-shock revealed significantly reduced freezing levels in MLC1 null mice (p = 0.01), indicating an impairment in contextual fear memory (Figure 7B). Specificity of the fear memory to the context was validated by placing the mice in a novel context B, which had different odor, color walls, and texture floor than the conditioning chamber, which should not illicit a freezing response. Regardless of genotype, mice froze significantly less in context B than context A, and no significant differences were seen in freezing levels between MLC1 null mice and WT in context B, indicating that the fear memory was specific to the context in which the shock was delivered.

MLC1 null mice were also tested for short-term working memory deficits using a T-maze. In the T-maze, mice were allowed to choose which of 2 arms of the maze to explore during 2 trials spaced 30 seconds apart. Mice with intact working memory have a preference to explore the arm not explored on the previous trial, which is scored as a successful alternation and can be expected to occur ~80% of the time in WT mice [360]. Over the course of 6 trials, WT successfully alternated an average of 77.8% of trials, whereas MLC1 null mice alternated 65.1% of trials (Figure 6C). This difference was not significant, but showed a trend towards impaired working memory in MLC1 null mice (p = 0.09). WT and MLC1 null mice showed equal levels of lateralization bias as they both chose to start the sample trial on the same side in approximately 4 of the 6 trials (Figure 7C), indicating that the trend towards impaired working memory in MLC1 null mice is not caused by differences in innate side-preference.

DISCUSSION

Evaluation of the experimental approach

Here we explored the regulation of astrocyte proteins after fear memory consolidation. For this analysis, astrocyte proteins with potential roles in fear memory consolidation were singled out by combining several data mining resources (Figure 1, Supplementary tables 1-3).

A list of astrocyte-enriched proteins served to restrict our analysis to proteins primarily of astrocytic origin [191]. Proteins regulated during fear memory consolidation, other hippocampal-dependent learning paradigms, or models of structural neuron-glial plasticity in the supraoptic nucleus further focused our list of candidates to those most likely to be involved in memory consolidation. A final refinement of these 212 candidates led us to pursue the expression patterns of 15 proteins during a time-course of fear memory consolidation. Of these 15 selected proteins, we found 11 significantly regulated, 1 showed a trend towards regulation, whereas 3 were not regulated during our time-course of fear memory consolidation. In addition, 11 of the 15 selected proteins were previously found differentially regulated between DS and IS at 4 hours (Chapter 5). Comparison with our immunoblotting data showed directional changes in protein levels could be confirmed for the large majority of these 11 proteins (Supplemental Figure 1). Together, this shows the validity of our selection approach, and the likelihood that many among the 212 candidate proteins will be regulated during fear conditioning as well.

To analyze astrocyte proteins potentially involved in synaptic regulation, we choose to isolate synaptosomes, which are frequently used to assess synapse-level changes in protein expression [183,359]. When isolated on a sucrose gradient, synaptosomes also contain glial proteins, including proteins known to be enriched in PAPs, suggesting these synaptosomes contain tripartite-synapse components (Chapter 3, Chapter 5). Use of a less purified fraction, such as total homogenate or P2+microsomes, would likely have masked changes specific to this subcellular compartment.

The synaptosomes were isolated exclusively from the dorsal hippocampus. Because lasting contextual memory is highly dependent on the dorsal hippocampus [361][362][363], we expected that any crucial molecular level changes involved in consolidation would be best detected in a preparation without contamination of the ventral hippocampus [364,365]. Nonetheless, when interpreting these results, it is crucial to bear in mind that the protein levels do not reflect protein content of the individual synapses involved in the fear memory consolidation.

Only a sparse population of neurons in the hippocampus actually encode the fear memory [260]. However, using a synaptosome preparation we were able to draw conclusions about global synaptic protein level changes, which may be conducive to the formation of a long lasting memory in a subset of cells. Accordingly, we expect that most astrocytic proteins contained in synaptosomes are not necessarily from tripartite synapses specifically related to the fear memory trace, but that they do offer insight into changes required in astrocyte networks to support the consolidation process. This view on more global changes in synaptic networks is of particular relevance to astrocyte proteins as astrocytes have been implicated in the regulation of synaptic scaling [START_REF] Stellwagen | Synaptic scaling mediated by glial TNF-alpha[END_REF]102]. In order to maintain equal firing rates of all neurons, mechanisms exist to tune the relative importance of synaptic connections each time a new connection, as a results of a new memory, is formed. Homeostatic synaptic scaling is a system-wide reorganization of synapses that prevents excessive synaptic potentiation or depression and maintains action potential firing within a functional dynamic range [366][367][368]. Astrocytes contribute to synaptic scaling by secretion of the cytokine TNF-alpha, which increases neuronal expression of AMPA receptors, thereby modifying synaptic strength [START_REF] Stellwagen | Synaptic scaling mediated by glial TNF-alpha[END_REF]102]. The synaptic scaling process begins in the moments after the initial synaptic potentiation and lasts for a period of days, although mechanisms of network-wide synaptic scaling remain largely elusive [368][369][370].

In order to measure astrocytic contributions to different aspects of memory consolidation we evaluated proteins during two phases of memory consolidation. The first phase, synaptic consolidation, lasts from the moment of memory acquisition until 6 hours later and is represented by the 4 hour time-point. This is followed by systems consolidation, which begins at 6 hours and can continue for years, and is represented by the 7, 24, 48, and 72 hour timepoints. A role in consolidation for one of the regulated astrocyte proteins, MLC1, was then validated by the detection of memory impairments in MLC1 null mice, linking changes in astrocyte protein expression in synaptosomes to functional consequences in memory consolidation.

Astrocyte proteins participate in different phases of fear memory consolidation

Many of the analyzed astrocyte proteins were found to be specifically down-regulated by fear memory consolidation during the protein-synthesis dependent window of consolidation (4 h time-point), whereas proteins specifically regulated by DS beyond the 4 hour time-point were rare (Figure 6). These DS down-regulated proteins included Ezrin, Ndrg2, Ppap2b and Atp1a2, with similar trends towards down-regulations for GAT3 and GLAST. In contrast, GFAP was the only protein found to be up-regulated within this window, whereas not being affected by the IS. It is interesting to note that GFAP, Ezr, and Ndrg2 all have documented roles in the regulation of astrocyte process morphology [138,228,371]. Increases in synaptic coverage of hippocampal synapses after stimulation have been observed [START_REF] Wenzel | The influence of long-term potentiation on the spatial relationship between astrocyte processes and potentiated synapses in the dentate gyrus neuropil of rat brain[END_REF]129], and motility of PAPs have been directly linked to effects on synaptic transmission [106]. As astrocytic coverage of synapses increases with potentiation, increased levels of PAP cytoskeletal proteins would be expected in synaptosomes from animals in the DS condition [129]. However, we observed decreased levels of Ezrin, which is a primary cytoskeletal constituent of PAPs [138]. Decreased levels of Ndrg2 have been shown to shorten astrocyte processes, which is also not in line with the expected increased astrocytic coverage of synapses after potentiation. The cytoskeletal protein GFAP was up-regulated, and although GFAP can modulate the morphology of larger astrocytic processes, it is not expressed in PAPs [137]. Together these observations suggest that the consolidation-specific protein regulations seen during synaptic consolidation may reflect global astrocyte morphological changes, not only those specific to potentiated hippocampal synapses. The precise subcellular location for the observed changes in protein levels remains to be determined.

In contrast to the 4 hour time-point, many of the expression level regulations that occurred outside the protein synthesis window (beyond 6 hours of consolidation) were observed along the diagonal between DS and IS groups (Figure 6). This dual influence by both DS and IS indicates that a large portion of these regulations is likely the result of foot shock-induced stress and not specific to the fear memory consolidation. Astrocytes abundantly express mineralocorticoid and glucocorticoid receptors, allowing them to respond to changes in stress levels [372]. Increased levels of GFAP are often seen in reactive astrocytes across a variety of stressful stimuli [373]. Cytoskeletal rearrangements of astrocytes in response to stress are well documented, and have recently been found to be caused by alterations in Ca 2+ wave dynamics which underlie increased release of certain gliotransmitters or increased astrocytic mobilization [374,375]. As astrocytic cytoskeletal rearrangements and release of gliotransmitters (including glutamate and ATP) are also involved in synaptic modulation [START_REF] Araque | Gliotransmitters travel in time and space[END_REF][START_REF] Wenzel | The influence of long-term potentiation on the spatial relationship between astrocyte processes and potentiated synapses in the dentate gyrus neuropil of rat brain[END_REF]129], similar regulations of astrocyte proteins in response to immediate shock stress or delayed-shock memory consolidation may be the result of activation of parallel pathways.

Interesting, we found large up-regulations of Ezrin, GS, and Bsg at 72 hours in the IS group, with less prominent up-regulations in the DS group. IS-specific up-regulations have previously been reported after 4 hours of consolidation (Chapter 5). It is difficult to imagine that such massive increases induced by the stress of the shock would not be comparable to what is found in the DS animals, which were exposed to the same stimulus. Furthermore, electrical activity of the hippocampus in fear conditioned rats, as measured by input-output curves, remains increased up to 7 days after training, but only in the DS and not in the IS group, suggesting the observed increase in astrocyte-protein levels is not due to on-going stimulation of the hippocampus in IS mice [376]. However, it has been proposed that stress and memory consolidation utilize the same synaptic pathways [267]. During the synaptic consolidation window it is possible that proteins which need to be up-regulated to solidify the stress response concomitantly need to be down-regulated to initiate synaptic consolidation of the associative fear memory, and vice versa. In other words, the late time point regulations seen for Bsg and GS in IS animals may be the product of molecular mechanisms set in motion by the shock and that were not diverted by the process of fear memory consolidation as occurred in DS animals. Another interesting observation is that even proteins seemingly regulated by IS stress can contribute to fear memory consolidation [377]. For example, Mlc1 showed downregulation in both IS and DS, suggesting stress of the shock to be the underlying cause, however behavioral analysis revealed MLC1 null animals had impaired expression of contextual fear memory. These global protein level regulations observed during the consolidation-time series may be a reflection of the astrocytic contribution to system-wide scaling processes. This hold true for proteins regulated by the IS as well as DS, as stress alone has been shown to modulate synaptic activity [377]. Astrocytes are organized into a functional syncytium connected by gap-junctions [378]. Calcium signal propagation through this astrocyte-network can relay information about the neuronal activity of one synapse to distantly located neurons, making astrocytes well poised to convey the signals necessary to orchestrating these global synaptic changes [START_REF] Serrano | GABAergic network activation of glial cells underlies hippocampal heterosynaptic depression[END_REF][START_REF] Navarrete | Endocannabinoids potentiate synaptic transmission through stimulation of astrocytes[END_REF]174]. The phases of astrocyte protein regulation seen over the course of consolidation may represent system-level synaptic dynamics governing the scaling process.

MLC1 is involved in fear memory consolidation

Mutations in the MLC1 gene are the primary genetic cause of megalencephalic leukoencephalopathy with subcortical cysts (MLC), a leukodystrophy affecting children [379] [380]. MLC1 is specifically expressed in astrocytes. Patients with mutations in the MLC1 gene experience profound white matter perturbations due to intramyelinic vacuole formation.

Magnetic resonance imaging shows swollen white matter with increased water content [380,381]. These white matter alterations caused by mutation of an astrocyte protein are thought to arise from imbalances in ion homeostasis. MLC1 is critical to the proper localization of CLC2, a chloride channel expressed in oligodendrocytes, and loss of MLC1 in astrocytes perturbs Cl - currents [382]. MLC1 has also been shown to participate in the regulation of cell volume via volume-regulated anion currents and calcium signaling [319,358]. Cells derived from MLC patients as well as MLC1-silenced astrocytes had impaired regulatory volume decreases after cell swelling was induced with hypertonic solution [319]. While most MLC patients retain cognitive function for many years, as the disease progresses, some can develop cognitive deficits [380]. Our finding that young adult MLC1 null mice have impaired contextual fear memory may indicate MLC cognitive pathology is accelerated in MLC1 mice or that they lack the compensatory mechanisms present in humans that delay the onset of cognitive decline.

What may be the mechanism underlying MLC1 regulated memory impairments? Astrocytes are well known for their regulatory volume changes in response to alterations in the extracellular environment, especially during increased extracellular K + levels after increased synaptic activity [383,384]. During bouts of neuronal activity, high levels of K + are excreted into the extracellular space [385]. Astrocytic Atp1a2, Na + -K + -Cl -cotransporter (NKCC1), and inward rectifying potassium channels serve to remove this excess K + to maintain base-line K + levels and modulate neuronal recovery rates after firing [386][387][388][389]. Compromising these astrocyte K + transporters leads to impaired synaptic function and is associated with various disease states [390][391][392][393]. This K + uptake is accompanied by an increase in cell volume, possibly as the result of water co-transport [394][395][396]. Because MLC1 deficient cells have impaired regulatory volume decrease, this may render MLC1 null mice unable to properly recover from K + -induced cell swelling, diminishing their K + clearance ability on subsequent neuronal firings and thus increasing baseline levels of neuronal excitability [385]. Sustained swelling could also increase the activity of volume-regulated anion channels (VRACs), which are known to release gliotransmitters, such as D-serine and glutamate, that subsequently modify synaptic transmission [START_REF] Araque | Gliotransmitters travel in time and space[END_REF][397][398][399]. This would cause erroneous synaptic modulations that deteriorate the fidelity of synaptic connections, potentially resulting in memory disturbances.

Increased volume of PAPs could also act as a physical barrier to diffusion, which would limit neurotransmitter spillover and hinder communication between synapses [START_REF] Sykova | Extrasynaptic volume transmission and diffusion parameters of the extracellular space[END_REF]. Astrocytic failure to appropriately respond to the ionic buffering needs of the synapse, imprecise release of gliotransmitters, and alteration of synaptic-cross talk over the course of consolidation could lead to contextual fear memory impairments observed in MLC1 null mice.

In sum, these promising results warrant further investigation of the role of MLC1 in synaptic function. It would be interesting to determine the localization of MLC1 proteins at perisynaptic astrocyte processes, and to evaluate MLC1 expression levels at earlier timepoints as an indication of whether memory impairment in MLC1 null mice may also be partially caused by a defect in synaptic consolidation. Also, the causal role of the protein in memory consolidation should be investigated after targeted gene expression rescue in the MLC1 null mice. Additionally, we show that fear memory consolidation is accompanied by dynamic changes in a wide range of astrocyte proteins, indicative for a possible role for astrocytes at different phases in the process of memory consolidation.

EXPERIMENTAL PROCEDURES

Mice

For the consolidation time series, 9 week old C57BL/6J male mice (Charles River, the Netherlands) were used. The generation of MLC1 null mice has been previously described [400]. For this study, MLC +/-mice were crossbred to generate MLC1 null and wild type littermate controls. All mice were individually housed on a 12 h light/dark cycle with ad libidum access to standard lab chow and water. All experiments were conducted with approval of the Animal Users Care Committee of the VU University Amsterdam.

Contextual fear conditioning

For the consolidation time series, fear conditioning was performed in a fear conditioning system (TSE, Bad Homburg, Germany, with integrated tracking software) consisting of a Plexiglas box (36 x 21 x 20 cm) and stainless steel floor grid (4 mm diameter, distance 9 mm) inside a dark grey housing unit. A white noise background (68 dB) and 120-500 lux light filled the box. The fear conditioning box was always thoroughly cleaned with 70% ethanol prior to testing of each animal. Mice were divided into 3 experimental groups. The base-line control animals were exposed to the context alone for 210 s but did not receive the foot shock (no shock, NS) and thus should not form any aversive memories to this context. It has been demonstrated that formation of an aversive contextual memory requires prior processing of the environment in order to form the associative memory between context and aversive stimuli [331]. Therefore, to control for the effects of stress from the foot shock alone, a second group of animals received the foot shock (2 s, 0.7 mA) immediately after being placed in the context box (immediate shock, IS), and were then allowed to explore for 210 s. Because the IS group animals do not have the opportunity to explore the box before they received the shock, they do not generate an aversive memory to this context [331]. A final group of mice was placed in the conditioning box and allowed to explore for 180 s prior to receiving the foot shock, allowing them time to process the environment and link this context to the aversive stimuli (delayed shock, DS). DS mice remained in the box for an additional 30 s after the shock.

All mice were returned to their home cage after a total of 210s in the conditioning box.

Hippocampi from 5 mice of each group were collected at 4 h, 7 h, 24 h, 48 h, and 72 h after training, without a memory retrieval session.

Behavioral experiments with Mlc1 null (n = 11) and wild type littermate (n= 15) mice [START_REF] Forsythe | Synaptic transmission: well-placed modulators[END_REF][START_REF] Reichenbach | Morphology and dynamics of perisynaptic glia[END_REF][START_REF] Nico | Morphofunctional aspects of the blood-brain barrier[END_REF] weeks old) were performed similarly to previous protocols, but without the auditory cueconditioning component [361,401]. The fear conditioning system (TSE, Bad Homburg, Germany) was comprised of two different context boxes (i.e. context A and context B).

Context A was identical to the condition context used in the consolidation time-series and training was performed the same as for DS mice. After 24 h, retention of the fear memory was tested by returning the mice to context A for 180 s, without foot shock. To ensure that any observed freezing behavior was context specific, 2 h after the retention trial in context A, mice were placed into a novel context B and freezing behavior was monitored for 180 s.

Context B consisted of the same Plexiglas box and white noise but with a solid plastic floor, white walls, and ambient white light from the experimental room. Context B was cleaned with 1% acetic acid prior to each trial to further differentiate the context. In all tests activity (cm/s) was measured by a photo beam detection system (10 Hz detection rate, resolution of 1.3 x 2.5 cm). Freezing was defined as lack of movement other than respiration and heart beat during a 5-s period.

T-maze

Short-term spatial memory was assessed in a T-maze (white PVC, arms 30 x10 cm) as previously described [360]. Sample trials began by placing a mouse into the start arm at the base of the T. During a sample trial, a removable central partition (17 cm long) protruded from the center of the back wall into the start arm, forcing mice to choose to explore either the left or right arm while positioned in the start arm. After an entry into the selected goal arm, a guillotine door at the entrance of the goal arm was lowered, and the mouse was contained in the arm for 30 s. In preparation for the test trial, the central partition was removed and a guillotine door at the end of the start arm was lowered. The test trial was initiated by placing the mouse in the start arm and removing both the guillotine door from the goal arm and the guillotine door at the end of the start arm. A successful alternation was scored if mice chose to enter the previously non-visited goal arm. A total of 6 sample and test trial cycles were performed, distributed across two days with at least 1 h in between.

Literature study to identify memory related astrocyte proteins

To identify proteins possibly involved in memory consolidation, a literature study was conducted to find proteins or genes regulated in hippocampal-dependent memory paradigms. In order to be included, a study had to perform either a microarray or proteomics analysis in rodents, use hippocampal tissue, and subject wild type animals to a hippocampaldependent memory task. Ten studies met these criteria and were included for further analysis [321][322][323][324][325][326][327][328][329][330]. Genes or proteins identified as regulated by a memory paradigm were compiled and sorted by frequency of observation. Because a large number of targets were identified only once, only targets identified in at least two studies were used for further screening steps.

Tissue isolation and preparation

Mice were sacrificed by cervical dislocation at the desired time point after fear conditioning.

The brain was removed and the dorsal hippocampi dissected on ice. Samples were immediately frozen on dry ice and stored at -80 °C until further use.

Synaptosome isolation was performed as previously described [183,359,402]. Briefly, frozen mouse dorsal hippocampi from each single mouse were homogenized in ice-cold 0.32 M sucrose/5 mM HEPES with added protease inhibitors (Roche). The homogenate was spun down at 1000x g for 10 min. to remove nuclei and cell debris. The resulting supernatant was applied to a discontinuous sucrose gradient and separated by ultracentrifugation at 110,000

x g for 2 h. The synaptosome fraction between 0.85 M and 1.2 M sucrose was harvested and pelleted by spinning at 25,000 RPM for 30 min. Samples were resuspended in 25 mM HEPES and the protein concentration determined by Bradford assay (Biorad).

Immunoblotting

Immunoblotting was performed essentially as previously described [183]. For each of the NS, IS, and DS conditions, 5 samples were used for each blot. Proteins were diluted in SDS sample buffer and denatured by heating to 98 °C for 5 min. Samples were separated on Criterion 4-12% TGX Stain-Free precast gradient polyacrylamide gels (BioRad) and then transferred to PVDF membrane (BioRad). Membranes were blocked in 5% skim milk Tris-buffered saline/ 0.05% Tween 20 (TBS-T) and incubated overnight with primary antibody at 4 °C. Primary antibodies against GFAP (1:1,000, Dako), Aldoc (1;1,000, Santa Cruz), Ezrin (1:1,000, GenScript), Basigin (1:1,000, Santa Cruz), Eno1 (1:500, Abcam), Glt1 (1:2,000, Millipore), Glyoxylase1 (1:1,000, Santa Cruz), Echs1 (1:500, Pierce), GAT3 (1:500, Abcam), GS (1:1,000, Genscript), Mlc1 (1:100, as described [357]), Ppap2b (1:1,000, a kind gift from Dr. Andrew Morris, University of Kentucky, USA), Atp1a2 (1:5,000, Millipore), GLAST (1:200,Santa Cruz),and Ndrg2 (1:200,Santa Cruz) were used. Appropriate polyclonal HRP-conjugated secondary antibodies were applied for 1 h at RT (Dako).

Probed membranes were incubated with SuperSignal West Femto Chemiluminescent substrate (Thermo-Scientific) and the reaction was imaged with a Li-Cor Odyssesy 2800 scanner. Blots were quantified using ImageStudio with background correction. To visualize protein loading for quantification normalization, gels were activated with UV-light prior to transfer using Gel Doc EZ Imager. ImageLab was used to quantify the optical density of the total protein content of each sample on the gel, which was then used as a loading control for the corresponding immunoblot.

Supplemental table 2: Candidates identified in at least 2 published consolidation studies

Background

The contributions of astrocytes to synaptic function have been recognized with the coining of the term "tripartite synapse", to acknowledge that the perisynaptic astrocyte process in close proximity to the synapse acts as a functional partner to synaptic transmission [START_REF] Araque | Tripartite synapses: glia, the unacknowledged partner[END_REF]. Although ample evidence exists demonstrating that astrocytes can respond to neurotransmitter release with intracellular Ca 2+ elevations that result in the release of neuromodulatory substances, many details of these mechanisms remain uncertain. This is due the heterogeneity of astrocyte functions across different brain regions and the use of different stimuli, as well as a lack of tools available to assess function of the very small diameter perisynaptic astrocyte processes (PAPs) contacting the synapse [START_REF] Araque | Gliotransmitters travel in time and space[END_REF].

In this thesis, I have analyzed the utility of several preparations for the identification of astrocyte proteins involved in the modulation of synaptic plasticity and then employed these techniques to identify proteins present in PAPs that are regulated by synaptic plasticity. I have evaluated two primary paradigms of plasticity: physiological stimulation (hyperosmosis and lactation) of the supraoptic nucleus and hippocampus-dependent contextual fear conditioning. The use of two different models allowed determination of which proteins are specific to one region, and which proteins are shared by plasticity mechanisms in both regions and thus potentially part of a more widespread astrocytic mechanism for synaptic control.

Comparison of 4 preparations for the determination of the roles of astrocytes at the tripartite synapse

The Cold-Jet

In Chapter 2, we developed the Cold Jet, an in vitro system for the separation of neurons and astrocytes in a co-culture. This technique identified several gene groups with altered expression in astrocytes due to the presence of neurons/synaptic activity, including those for lipid metabolism and cell adhesion, which both have verified role in tripartite synapse function [153,403]. The major benefits of the Cold Jet are that it allows for a high-level of purification of astrocytes from neurons and as a more simplified model of in vivo neuron-glia interactions lends itself well to manipulations for initial steps towards resolving molecular mechanisms. However, the advantages of an in vitro system can also be disadvantages as they may not accurately reflect the in vivo system. In the case of the Cold Jet, low protein yield means analyses must be conducted at the transcriptional level, rather than on translated proteins. As transcript levels do not necessarily correlate with levels of translated protein, this adds an additional layer of discord between measurements and actual biological function.

These gene-level measurements represent transcriptional changes throughout the cell, and are thus not specific to membrane proteins or proteins preferentially targeted to PAPs.

Indeed, the 4 genes validated by qPCR as regulated in astrocytes after co-culture (Insig1, Hmgcs, Dhcr7, and Erg1), are not plasma membrane proteins, therefore not likely expressed in PAPs, and accordingly were not detected at the protein level in sucrose-isolated synaptosomes. Although co-cultures of 10-14 DIV do show synaptic activity [162], it is not known if these cultures contain PAPs or structural tripartite synapses as found in the intact brain. This technique is therefore not suitable for specific evaluation of PAP proteins, but instead is powerful for identification of transcription changes in astrocytes in response to neurons.

Percoll®-isolated synaptosomes and gliosomes

In Chapter 3, TripleTOF label-free mass spectrometry was used to determine the purity of Percoll®-isolated synaptosomes and gliosomes from freshly dissected hippocampi (Figure 1A).

Immunoblotting showed that gliosomes and synaptosomes were both enriched for some known PAP and synapse markers, respectively, however, detailed mass spectrometry analysis of the proteins enriched in each preparation revealed that synaptosomes were enriched for 4 out of 15 known PAP marker proteins while gliosomes were only enriched for 1 PAP marker protein. We therefore concluded that although gliosomes prepared in this fashion are enriched for PAPs compared to the homogenate, the low level of enrichment and low protein yield was not the best option for evaluation of PAP levels in further studies.

Interestingly, preliminary data acquired from mass spectrometry analysis of gliosomes and synaptosomes isolated from frozen material, rather than fresh, results in a much higher degree of enrichment. Gliosomes derived from frozen material contain significantly (FDR ≤ 10%) more PAP proteins (the following 8: GLAST, GS, GP-M, GP-B, ACSBG1, NHERF1, Cx43, see Chapter 3, Table 5) than frozen synaptosomes, whereas frozen synaptosomes were not enriched for any PAP markers compared to frozen gliosomes. Ice crystals formed during the tissue freezing process likely burst plasma membranes and disrupt cell connections, decreasing the amount of intact synaptosomes while increasing the amount of non-resealed membranes. If adhesion of astrocyte membranes to the synapse are "loosened" by the freezing process, perhaps this facilitates their segregation into the gliosome-enriched Percoll® density gradient resulting in the increased enrichment of PAP markers in gliosomes isolated from frozen material. Although our mass spectrometry results on frozen material require further validation, they are a promising indication that gliosomes derived from frozen tissue may be highly enriched for PAP proteins and useful for the detection of novel PAP proteins, as well as a fraction suitable for evaluation of changes in PAP protein levels during experimental manipulations.

Pellet 2 + Microsomes (P2+M)

In Chapter 4, Orbitrap label-free mass spectrometry was used to search for proteins regulated by function-dependent plasticity in the supraoptic nucleus (SON). Due to the small size of the SON, a crude membrane fraction, the P2+M, was used instead of a more purified fraction, such as synaptosomes or gliosomes (Figure 1). Although the P2+M preparation does contain synapses and PAP membranes, it also contains plasma membranes, intracellular membranes (endoplasmic reticulum, Golgi apparatus, lysosomes, etc.), myelin, and free mitochondria from all regions of all cell types. To combat the complexity of this sample, we employed a bioinformatics filtering step with the use of the astrocyte-enriched gene list to ascertain which of the measured proteins were of astrocyte origin. However, this approach excludes evaluation of any astrocyte proteins that are also expressed in other cell types, resulting in a narrowed picture of the true biological picture. Furthermore, the complexity of the sample required it to be divided over 12 mass spectrometry runs per sample, thereby inducing a large degree of technical variation. Because plasticity changes generally only affect a subset of cells, for example, only the oxytocin neurons in the SON [START_REF] Chapman | Osmotic stimulation causes structural plasticity of neurone-glia relationships of the oxytocin but not vasopressin secreting neurones in the hypothalamic supraoptic nucleus[END_REF][START_REF] Theodosis | Structural plasticity in the hypothalamic supraoptic nucleus at lactation affects oxytocin-, but not vasopressin-secreting neurones[END_REF], protein expression changes related to these specific synapses may be lost in the noise of a fraction as complex as the P2+M. Therefore, using this analysis method, the P2+M fraction lacks the subcellular specificity required for detailed and reliable examination of PAP proteins. A better option would be to use a newer generation mass spectrometer with higher sensitivity that would allow samples to be divided over fewer run, thus reducing technical variation. A lower input material requirement would also allow for further subcellular fractionation of samples into a synaptosome preparation which would give more insight into the protein regulations specifically at the synapse.

Sucrose-isolated synaptosomes and synaptic membranes

Although our analysis in Chapter 3 demonstrated that gliosomes do contain astrocyte membrane proteins, there was also a considerable degree of contamination from the synaptic compartment. Due to this contamination and the low yield produced by the gliosome isolation procedure, we elected to use sucrose-isolated synaptic membranes and synaptosomes for the subsequent analyses presented in Chapters 5 and 6, respectively (Figure 1C,D). Sucrose-isolated synaptosomes are less purified than their Percoll®-isolated counterparts and therefore contain abundant quanties of glial proteins, including those from the PAPs. Sucrose-isolated synaptic membranes, a membrane-enriched fraction derived from sucrose-synaptosomes, also contain PAP proteins. The proteomic data from Chapters 5 and 6

illustrate the usefulness of sucrose-synaptosomes and synaptic membranes for analysis of PAPs as known PAP marker proteins (including GLAST, Ezrin, Glt1, and Ndrg2) are easily detected by both mass spectrometry and immunoblotting. A large advantage of sucrose synaptosomes over gliosomes is the 10-fold increase in material yield, allowing fewer animals to be used and more down-stream analyses to be conducted.

Sucrose-synaptosomes are generally thought of as a fraction enriched for the resealed presynaptic terminal and post-synaptic membrane which are often used to assess synaptic function [183,359,404]. Although sucrose-synaptosomes contain PAPs, it is not known if these astrocyte membranes remain physically attached to the synaptic elements or are separate contaminating membranes. Synaptosomes depleted of glial contaminants can be isolated using fluorescence activated sorting, suggesting many synaptosomes exist without an affixed astrocyte membrane [264], but the reliable detection level of particular PAP proteins in sucrose-synaptosomes also suggests their presence in not just random contamination. Immunoelectron microscopy studies labeling synaptosome preparations for PAP and synaptic markers will help to resolve whether synaptosomes exist as "tripartitesynaptosomes" and if not, what the morphology is of the present astrocyte membranes.

Similarly, immunoelectron microscopy on synaptic membrane samples will help to determine if astrocyte membrane remain attached to the synaptic membranes even after the osmotic shock treatment. Overall, of the preparations analyzed in this thesis, we found sucrosesynaptosomes to be the best balance of purity to material yield. They are a fraction that contains measurable levels of PAP proteins, are depleted of many non-synaptic proteins, and they can be derived from a manageable amount of input material and are thus suitable for behavioral and intervention studies.

Similarities and differences between models of synaptic plasticity

We evaluated astrocyte proteins in the SON during lactation and hyperosmosis-induced morphological plasticity and, and in the hippocampus during contextual fear conditioning. In the SON, stimulation results in the retraction of astrocyte processes around oxytocin neurons that decreases astrocyte coverage of neurons and facilitates the induction of LTP at biologically-relevant bursting synapses [107]. Conversely, after induction of LTP in the hippocampus, current evidence suggests that astrocyte processes extend and increase their coverage of synapses [START_REF] Wenzel | The influence of long-term potentiation on the spatial relationship between astrocyte processes and potentiated synapses in the dentate gyrus neuropil of rat brain[END_REF]129], although in recent unpublished works the opposite effect has been reported [405,406]. At first glance it appears that opposite effects of astrocyte structural plasticity are seen in these two brain regions. However, astrocyte retraction in the SON only induces LTP at synapses with strong burst firing activity, while those with high, although not bursting, firing rates undergo LTD in the absence of glial-derived D-serine [105].

Together these observations suggest that a specialized high-pass filter exists in the SON to amplify particular responses, and that, in general, close proximity of astrocyte process to the synapse increases gliotransmitter availability and promotes synaptic potentiation. However, clarification of the contradictions regarding whether astrocyte processes extend or retract from potentiated synapses is required for a more clear interpretation of these results.

Despite the mechanistic differences in the control of synaptic plasticity between the hippocampus and SON, it is likely that proteins exist that are important to tripartite synapse function in both models and furthermore in other brain regions. Therefore, during our selection of proteins to evaluate during the fear memory consolidation series in Chapter 6, we also considered proteins that had been regulated by plasticity in the SON. MLC1 was 1.65

-fold down-regulated by hyperosmosis (22% FDR), but has not been detected in the Chapter 5 iTRAQ consolidation experiment. Nonetheless, we included this protein in our consolidation time-series evaluation and found that MLC1 expression is also down-regulated in the hippocampus by fear memory consolidation. Behavioral testing of MLC1 null mice revealed contextual fear memory impairment, illustrating that the same astrocyte proteins can be involved in a variety of plasticity responses across the brain.

Perspectives on astrocyte proteins during contextual fear memory consolidation

Most changes in astrocyte proteins specific to fear memory consolidation occurred at the 4 hour time-point. Increased contact between PAPs and synapses in hippocampal slices have been seen 30 minutes after stimulation, were highly pronounced 8 hours later, and returned to control levels after 48 hours [START_REF] Wenzel | The influence of long-term potentiation on the spatial relationship between astrocyte processes and potentiated synapses in the dentate gyrus neuropil of rat brain[END_REF]129]. Therefore, at 4 hours after contextual fear conditioning we expected to see an increased astrocyte ensheathment of potentiated synapses, and thus an increased amount of PAP proteins in our synaptosome preparations.

Additional roles for astrocyte-expressed proteins in synaptic plasticity

In each of our analyses, we focused on "astrocyte-enriched" or "astrocyte-specific" proteins based on data obtained from published literature. Due to the large amount of data generated by mass-spectrometry approaches, this filtering criteria was necessary to narrow down candidates that were most likely to be astrocyte proteins for further evaluation. However, many proteins were not selected in our screens because they are expressed by multiple cell types, but one cannot exclude the possibility that the protein's presence in astrocytes mediates effects on synaptic plasticity. For example, the cannabinoid type- Although they were not the focus of the analyses in this thesis, the existing data can be reanalyzed with a focus on PTMs to point towards additional roles for proteins without expression level regulations that still may be involved in plasticity.

Conclusions and perspectives

Using a variety of experimental paradigms we have shown that expression levels of astrocyte proteins change in response to plasticity events that include both structural and synaptic plasticity. MLC1 proved to be involved in memory consolidation, demonstrating that our proteomic approach are useful for identifying astrocyte protein regulations that are biologically relevant. Further experiments to re-introduce MLC1 into null animals will be required to definitively show the importance of MLC1 to synaptic consolidation.

Demonstrating whether or not MLC1 is expressed on PAPs will help to determine whether its effects of plasticity may be via direct modulation of the synaptic space or by more indirect mechanisms, such as regulation of cell volume that alter the function of other synapseexpressed channels.

In addition to MLC1, 11 other candidate proteins were shown to have differential regulations over the course of fear memory consolidation and are thus interesting targets to pursue in further studies. The initial list of 212 candidates from which these targets were derived is a rich source of astrocyte proteins with potential involvement in synaptic function. Preliminary results suggest a minor alteration to the gliosome preparation protocol, using frozen rather than fresh tissue, can significantly improve the purification of PAP membranes. Optimization of this technique should be pursued as the use of frozen starting material is more practical for paradigms involving manipulations and improves reproducibility. Together, the techniques developed here and the protein target lists generated can be utilized to further our knowledge of astrocytic functions at the tripartite synapse.

ENGLISH SUMMARY Proteomic characterization of perisynaptic astrocytes in synaptic plasticity

Astrocytes are the most abundant cell type in the brain and mediate a myriad of functions, including neurogenesis, synaptogenesis, ion homeostasis, metabolic support, formation of the blood-brain barrier, clearance of toxic substances and responses to brain injuries. Fine astrocyte processes contacting synaptic elements, termed perisynaptic astrocyte processes (PAPs), express at their surface receptors and transporters to monitor synaptic activity and contain specialized protein machinery that allows them to respond to this activity by the release of signaling substances, called gliotransmitters. The capacity of astrocyte to actively regulate synaptic function has led to their formal recognition as active synaptic elements with the coining of the term "tripartite synapse", a concept which includes the neuronal pre-and postsynaptic compartments as well as the PAP.

Astrocytes are able to respond to a variety of transmitters, including glutamate, acetylcholine, GABA, and ATP, indicating they are involved in a vast array of synaptic processes.

Furthermore, alterations in tripartite synaptic function have been linked with neurological disorders such as epilepsy, depression, dementia, amyotrophic lateral sclerosis, and schizophrenia, and thus the continued study of astrocytic contributions to synaptic function are of clinical and societal relevance. However, progress in this important field has been hindered by a lack of existing tools that can reliably evaluate the function and content of PAPs due to their extremely small (<1 µm diameter) nature. Therefore, there is a need for additional techniques that can give insight into PAP function and be used for evaluation of PAPs in experimental paradigms. In this thesis I have evaluated the potential utility of several preparations for the assessment of astrocyte proteins involved in the regulation of synaptic plasticity, and employed the most suitable of these preparations to measure regulation in astrocyte protein levels in models of synaptic plasticity.

In Chapter 2, I have developed an in vitro system for the analysis of genes induced in astrocytes by the presence of neurons, named cold jet. In this technique, application of as ice cold stream of phosphate-buffered saline to mature neuron-astrocyte co-cultures results in the preferential removal of neurons due to differences in adhesion characteristics between cell types. With this method, populations of astrocytes that had been exposed to neuronal secreted, adhesion, and electrical signals could be separated from neurons with approximately 90% purity. Microarray analysis of purified astrocytes from co -cultures, compared with astrocyte alone, indicated many genes involved in metabolic functions were regulated, among which Insig1, Hmgcs, and Dhcr7, genes involved in lipid metabolism, that we found down-regulation in astrocytes when cocultured with neurons.

As transcription-level changes do not necessarily correlate with protein expression levels and thus biological function, I proceeded to evaluate other biochemically isolated preparations using proteomics techniques. Gliosomes are a Percoll°-gradient isolated fraction that are enriched for several astrocyte marker proteins, including PAP proteins, and thus could potentially be of use for the identification of novel PAP proteins and to monitor PAP protein changes during experimental paradigms. In Chapter 3, I have used label-free TripleTOF mass spectrometry to conduct a more detailed evaluation of the proteins enriched in gliosomes.

For comparison, proteins enriched in Percoll°-isolated synaptosomes were also measured.

Gliosomes were enriched for G-protein mediated signaling proteins whereas synaptosomes were enriched for proteins involved in vesicle-mediated transport. Surprisingly, although gliosomes were enriched for some PAP marker proteins, synaptosomes were enriched for more PAP proteins.

After evaluation of the appropriateness of these techniques for assessment of PAP properties, I proceeded to assess contributions of astrocytes to synaptic plasticity using several in vivo models. The supraoptic nucleus (SON) is well-known for its functional and structural neuronalglial plasticity changes induced by lactation and hyperosmosis. During these physiologically stimulating conditions, astrocyte processes around the synapses onto oxytocin magnocellular neurons are retracted, thereby altering the diffusion properties and gliotransmitter availability of the synaptic cleft. This structural plasticity results in altered synaptic plasticity that mediates the physiological changes needed in response to hyperosmosis and lactation.

In Chapter 4, I used Orbitrap label-free mass spectrometry to quantify changes in protein expression between virgin, lactating, and hyperosmotic animals in order to elucidate components of the mechanism underlying these plastic changes. Mass spectrometry identified 985 proteins significantly regulated by plasticity and subsequent filtering steps were employed to narrow this list down to the 86 proteins most likely to be present in astrocyte and involved in plasticity changes. Unfortunately, immunoblotting was unable to validate most of the measured regulations, although N-myc down-stream regulated gene 2 (Ndrg2)

was significantly up-regulated by hyperosmosis and lactation by both mass spectrometry and immunoblot analysis. I concluded that there were likely many sources of biological and experimental variation in this approach which resulted in the inability to validate the mass spectrometry results, but discussed several possibilities for methodological improvement to facilitate the proteomics-assisted detection of SON structural remodeling related proteins.

Another well-studied model of synaptic plasticity is contextual fear conditioning. Mice that are allowed to explore a conditioning chamber prior to receiving a foot shock learn to associate the aversive stimulus with the context in which it was received. Interestingly, mice that receive the foot shock before being given the opportunity to explore the context, do not form an aversive memory to the context and serve as a control for the stressful effects of the shock itself, thus effects on synaptic plasticity specific to fear memory consolidation can be determined. As such, in Chapter 5, we report that at 4 hours after training, 423 proteins were significantly regulated, with 164 regulated by the delayed shock and 273 regulated by the immediate shock. Regulations of astrocyte proteins were observed, including DS-specific down-regulations at 4 hours for glutamate transporters GLAST and GLT-1 and the astrocyte GABA transporter Slc6a11/GAT3. Together, this analysis demonstrated firstly that the IS alone is capable of inducing large protein expression changes, and secondly that astrocyte proteins are regulated by the IS and well as specifically by fear memory consolidation.

With the promising evidence that PAP proteins were regulated by fear memory consolidation, in Chapter 6 we further explored the levels of a selection of astrocyte proteins during different phases of contextual fear conditioning. Most proteins were found to be specifically regulated by fear memory consolidation at 4 hours. Decreased expressed of MLC1 was seen at 24h. To determine if this protein regulation may be of biological significance, MLC1 null mice were tested in behavioral paradigms. MLC1 null mice frozen significantly less than wild type littermates when tested for contextual fear memory after 24 hours, indicating impairments in fear memory consolidation. This work demonstrated the dynamic nature of astrocyte protein expression over the course of consolidation and points to a role for astrocyte proteins in the modulation of memory consolidation.

Cumulatively, in this thesis I have characterized several preparations that can be used to evaluate astrocyte contributions to synaptic plasticity and identified numerous astrocyteenriched proteins regulated by synaptic plasticity that can be targeted in future studies to elaborate upon the mechanisms of action of the tripartite synapse in both physiological and pathological contexts.

NEDERLANDSE SAMENVATTING Proteomische karakterisatie van perisynaptische astrocyten in synaptische plasticiteit

Astrocyten zijn de meest voorkomende cellen in de hersenen en hebben een veelvoud aan functies, waaronder een rol in neurogenese, synaptogenese, ion homeostase, metabole ondersteuning, vorming van de bloed-hersen barrière, opruimen van toxische stoffen en de reactie op hersenbeschadigingen. De astrocyt heeft contact met de synaps via kleine cellulaire uitlopers, genaamd perisynaptische astrocyt projecties (PAPs), met daarop receptor-en transport-eiwitten die synaptische activiteit kunnen detecteren. Tevens bevatten de PAPs gespecialiseerde eiwitten die het mogelijk maken om op synaptische activiteit te reageren met de afgifte van signaalstoffen, genaamd gliotransmitters. Dit vermogen van astrocyten om de synaptische functie te reguleren heeft geleid tot het formeel erkennen van de astrocyt als actief element van de synaps, en daarmee tot de term 'tripartite synaps', het concept dat de synaps bestaat uit een neuronale pre-and post-synaptische terminal en de PAP.

Astrocyten kunnen reageren op verschillende neurotransmitters, waaronder glutamaat, acetylcholine, GABA, en ATP, wat aangeeft dat astrocyten een rol kunnen spelen in meerdere synaptische processen. Daarnaast is gevonden dat de functie van tripartite synapsen veranderd is in verschillende neurologische afwijkingen, zoals epilepsie, depressie, dementie, amyotrofe laterale sclerosis, en schizofrenie, wat het klinische en maatschappelijke belang aangeeft van onderzoek aan de rol van astrocyten bij de synaps. Echter, de vooruitgang in dit onderzoeksveld wordt ernstig vertraagd door het gebrek aan geschikte onderzoeksmethodes om de functie en samenstelling van PAPs te kunnen bestuderen, dit omdat PAPs extreem klein zijn (<1 µm in doorsnede). Het is daarom van groot belang dat nieuwe technieken worden ontwikkeld voor de analyse van PAP functies in verschillende experimentele modellen. In dit proefschrift heb ik verschillende isolatie-methoden onderzocht op hun geschiktheid voor de analyse van PAPs, en heb vervolgens de meest geschikte methode gebruikt voor de analyse van PAP eiwitten in modellen van synaptische plasticiteit.

In hoofdstuk 2 hebben we een in vitro methode ontwikkeld, genaamd 'cold jet', voor de In hoofdstuk 6 zijn we vervolgens verder gegaan met het onderzoeken van de expressie van een selectie van astrocyt-eiwitten op verschillende momenten in het opslagproces van angstherinneringen. Voor de meeste astrocyt-eiwitten werd een afname in niveaus gevonden na 4 uur. Voor het MLC1 eiwit werd een afname gevonden na 24 uur, en om het belang van deze MLC1 regulatie te bepalen werden Mlc1 knockout muizen getest op geheugen.

Gevonden werd dat MLC1 ko muizen een significant minder goed contextueel angstgeheugen hebben dan controle muizen. Tezamen laten deze data zien dat astrocyt-eiwitten dynamisch gereguleerd worden tijdens het opslaan van angstherinneringen, en tevens dat astrocyteiwitten vermoedelijk een regulerende rol hebben in het opslaan van herinneringen in het geheugen.

Samenvattend, in dit proefschrift heb ik verschillende biochemische preparaten onderzocht op hun geschiktheid voor het bepalen van de rol van astrocyt-eiwitten in synaptische plasticiteit, ik heb een groot aantal astrocyt-eiwitten gevonden die gereguleerd worden door synaptische plasticiteit en die geschikte kandidaat-eiwitten zijn om in vervolgstudies te manipuleren en daarmee moleculaire mechanisme te onderzoeken die ten grondslag liggen aan het functioneren van de tripartite synaps in zowel fysiologische als pathologische omstandigheden. for all the international plane tickets to spend the holidays with my family, and Uncle George, thanks for all advice about life in Europe and social connections.

RÉSUMÉ EN FRANÇAIS

To my supervisors Mark, Guus, and Stéphane, thank you for this unique opportunity and your dedication to making this international collaboration work. As we all know, there have been many complications along the way, but somehow we made it! Even if had I know that when you told me I would be a "pioneer' by taking Twinning Cum Laude PhD that what you really meant was I would be the first guinea pig in the pilot experimental protocol I would have eagerly taken the position nonetheless. Mark, your endless optimism throughout this process has been much needed and appreciated. Thank you for tolerating my constant worrying and critical view of everything and everyone, especially of myself. I am certainly a better scientists after our 3.5 years of regular meetings and discussion. Guus and Stéphane, I am grateful that you have allowed me to prod you for all your scientific expertise and contacts and for all the rapid e-mail replies even into the wee hours.

To Sabine and Ka Wan, although you were not my "official" supervisors I am very appreciate of all the time, energy, and consideration you have invested in me. The knowledge I now have about behavioral analysis and fear conditioning as well as mass spectrometry and proteomics Lody, it was wonderful getting to know you during all the downtime while learning to culture astrocytes and I appreciate all the tips and tricks you have shared with me.

Brigitte, the whole lab would fall apart without you. I am so thankful for all the assistance you have given me over the years while trying to sort out the mountains of paperwork associated with my complicated employment situation. Thank you for always jumping through hoops and finding ways to make sure I got my salary and refunds.

To the generation before me, Ning, Pieter, Marlene, and Nutabi thank you for the basic skills to organize all the party festivities. Bart, it is a little bit disgusting how you can be so happy all the time, but it is a wonderful quality and I am grateful you shared that happiness with me.

Priyanka (and Martin) thank you for always bringing the party atmosphere and setting an excellent example of how even married couples can be awesome and exciting.

To all my other colleagues who have helped me along the way or taken the time to chat with me in the coffee room, Mariette, Sygrid, Riejanne, Roeland, Reka, Frank, Desiree, Tatiana, Emiel, Nienke, Francesca, Sonya, Anna, Rob, Sara, thank you for making the VU feel like a

  less efficient at 14 DIV (Fig 1). Cold jet removal of neuronal mRNAs was tested by microarray RNA profiling, which confirmed robust removal of neuronal mRNAs, e.g. Mapt, Gap43, Snap25 and Snca (Fig 2A). Neuron-enriched mRNAs are expected downregulated in coculture by cold jet, while being absent or low expressed in astrocytes. To determine the efficiency of the cold jet in removing these neuron-enriched mRNAs, their fluorescence levels in CC and CC-CJ was taken and the level in astrocytes substracted. The data indicated that the cold jet effectively removed approximately 90% of neuronal RNA (Fig. 2B).

Figure 1 .

 1 Figure 1. Efficiency of cold jet on neuron-astrocyte co-cultures after 10 and 14 DIV. (A) Neuron-astrocyte co-culture at 10 DIV. (B) At 10 DIV, cold jet removed almost all neuronal cell bodies and only a few residual neurites were detected (white arrow), note that the astrocyte cell layer remained unchanged. (C) Co-culture after 14 DIV. (D) At 14 DIV, cold jet removed neurites but less efficiently than at 10 DIV. Shown is an example were many residual neurites are observed (white arrows), probably representing clusters of firmly attached neurons.Neuron-astrocyte co-cultures with or without treatment with cold jet, were stained for the neuronal marker MAP2 (green), the astrocyte marker GFAP (red) and nuclei (DAPI, blue) to count the number of somas (E) and neurites (F). Numbers show average and SEM for 3 tissue culture wells. Scale bar 100 µM (A).

Figure 2 .

 2 Figure 2. Efficiency of cold jet to remove neuronal mRNA from co-cultures. (A) Samples from neuron-astrocyte co-cultures, with or without treatment with cold jet, were collected and mRNA levels for neuronal mRNAs were analysed using microarray. Shown are 40 of the most down-regulated transcripts in co-culture as a result of the cold jet procedure. Each individual gene expression level was normalized (see methods) and plotted on a Log2 scale, green representing decreased mRNA levels in CC-CJ samples compared to CC alone (B) Normalized fluorescence levels were compared and expressed as percentage of the levels present in coculture samples. Error bars represent standard error of the mean (SEM).

Figure 3 .

 3 Figure3. Microarray analysis of strongest regulated neuron-responsive astrocyte genes. Shown are 36 of the most up -and down-regulated transcripts in astrocytes when co-cultured with neurons, which were subsequent removed by cold jet treatment (CC-CJ), compared to astrocytes cultured alone (AA-CJ). Each individual gene expression level was normalized (see methods) and plotted on a Log2 scale with red representing increased and green decreased mRNA levels in CC-CJ astrocytes compared to astrocytes cultured alone (AA-CJ).

  significantly enriched for the NMDA-receptor subunit 1 (GluN1, F(2,6) = 21.5, p = 0.002)and PSD-95 (F(2,6) = 5.75, p = 0.04), as expected[179, 188]. Post-hoc analyses determined that GluN1 was 2.3-fold more abundant in synaptosomes than in gliosomes, and 1.8-fold enriched compared to the homogenate (Figure2 A,B). The levels of these neuronal proteins in

Figure 1 :

 1 Figure 1: Isolation procedures. Schematic depiction of the protocol for isolation of purified synaptosomes and gliosomes using a Percoll ® gradient which were analyzed by TripleTOF mass spectrometry.

Figure 2 :

 2 Figure 2: Enrichment of marker proteins for gliosomes and synaptosomes compared with homogenate. Immunoblots (A) and quantification (B) for the neuronal marker proteins beta tubulin 3 (BT3), NMDA receptor subunit 1(GluN1), and post-synaptic density protein 95 (PSD-95). Immunoblots (C) and quantification (D) for the astrocytic marker proteins glial fibrillary acidic protein (GFAP), ezrin (Ezr), and vesicle-associated membrane protein 3 (VAMP3). Bar graphs depict the mean ± SEM. * p < 0.05, ** p < 0.005.

Figure 3 :

 3 Figure 3: Data quality analysis. A) Histograms depicting the enrichment ratios seen in experimental comparisons of two different preparations compared with the background ratios induced by technical variation between samples of the same type. Red line indicates the zero point. B) Euclidean distance was used to cluster samples based on total protein abundance. The dendogram demonstrates that no samples violate the cluster boundaries. C) Scatter plot showing the distribution of protein ratios by intensity. Vertical lines indicate the bin boundaries used for statistical analysis. Each bin contain approximately 300 proteins.

Figure 4 :

 4 Figure 4: Validation of TripleTOF mass spectrometry by quantitative immunoblot. Expression levels of proteins identified as significantly gliosome-(A) or synaptosome-(B) enriched (FDR <10%) were evaluated on independent sample pairs (n= 6) using immunoblot. Bars represent log2 enrichment ±SEM as quantified by mass spectrometry and immunoblot for depicted proteins. Representative blots of a synaptosome (S) and gliosome (G) sample with respective loading control are below each graph. * p ≤ 0.05, ** p ≤ 0.005, *** p ≤ 0.0005.

  As glutamatergic volleys onto oxytocin neurons underlie the lactation-associated burst firing pattern required for the milk-ejection reflex [215], this disinhibition may serve to facilitate information transfer at highly-active synapses conveying lactation stimulus-related signals [105]. Additionally, retraction of astrocyte processes from the synapse decreases availability of the NMDA-receptor co-agonist D-serine, which is produced exclusively by astrocytes in the SON. Reduced D-serine levels alter the NMDA receptor activation probability, thus requiring higher levels of glutamatergic input (i.e volleys conveying lactation information) to induce potentiation of the synapse [107]. Together these consequences of astrocyte retraction establish a high-pass filter whereby only synchronized glutamatergic input from lactation stimulation will result in the oxytocin neuron burst firing sufficient to induce the milk-ejection reflex [216].

Figure 1 :

 1 Figure 1: Data quality analysis of SON from virgin (V), hyperosmotic (H), and lactating (L) rats analyzed by Orbitrap label-free mass spectrometry. A) Number of proteins per falsediscovery rate (FDR) level in the multiclass analysis. B) Number of proteins per FDR in the pairwise virgin-hyperosmotic comparison. C) Number of proteins per FDR in the pair-wise virginlactating comparison. D) Clustering of experimental groups based on protein intensity using Pearson correlation distance. E) Cluster of individual samples based on protein intensity using Euclidean distance.

Figure 2 :

 2 Figure 2: Protein clustering for selection of plasticity-related proteins. A) Expected expression pattern of proteins not regulated by hyperosmotic or lactating condition, regulated only by hyperosmotic condition, regulated only by lactating condition, differentially regulated by hyperosmotic and lactating conditions, and regulated by hyperosmotic and lactating conditions in the same direction, indicating a possible role in structural plasticity. B) Clustering of 985 significantly regulated (FDR ≤ 22%) proteins by Pearson correlation distance of summed peptide intensity into 16 clusters. Each colored line represents the expression pattern of a single protein. Clusters in green boxes ("up-clusters") and clusters in red boxes ("down-clusters") represent groups of proteins regulated by SON functional plasticity.

Figure 3 :

 3 Figure 3: Schematic of proteomic data analysis towards identification of astrocyte-specific structural plasticity related proteins.

  metabolic process' protein lipid phosphate phosphohydrolase 3 (Ppap2b) immunoblots did not confirm the up-regulation by plasticity (F(2,12) = .05, p = 0.95). 'Transport' proteins aquaporin 4 (Aqp4), Intersectin1 (Itsn1), and GABA-transporter 3 (GAT3) immunoblots also did not match the mass spectrometry results. However, GAT3 regulations were border-line significant (F(2,12) = 3.19, p= 0.07) and showed a strong trend toward up-regulation by hyperosmosis (1.14 -fold, p = 0. 03) and lactation (1.11 -fold, p = 0.09) which would be in line with the mass spectrometry data. Regulation of Aqp4 was significant (F(2,12) = 4.10, p= 0.05), but in disagreement with the mass spectrometry results as Aqp4 expression by immunoblot was down-regulated in hyperosmotic compared with both virgin (-1.20 -fold, p = 0.04) and lactating (1.23-fold, p = 0.03) animals. Thus, we were not able to satisfactory validate the mass spectrometry results for astrocyte proteins.

  7-fold upregulated by hyperosmosis with an 8% FDR. Immunoblotting confirmed Ndrg2 regulation (F(2,9) = 4.87, p = 0.03) with a 1.4 fold increase in expression by hyperosmosis (p = 0.02), as well as a 1.4-fold increased expression in lactating samples (p = 0.02), suggesting it has a possible role in SON plasticity (Figure 4C).Validation using mass spectrometry samplesBecause validation of the mass spectrometry results on independent samples was generally unsuccessful, we attempted to confirm the regulations with immunoblotting on the same samples as were used for mass spectrometry analysis. This would indicate a possible problem with the independent replicates or potential complications with mass spectrometry data acquisition. Blotting for Ndrg2, GAT3, and Itsn1 on the same samples analyzed by mass spectrometry did not confirm the mass spectrometry results (Ndrg2: F(2,9) = 2.99, p = 0.10; GAT3: F(2,9) = 0.44, p = 0.66 ; Itsn1: F(2,9) = 0.54, p = 0.60), although Ndrg2 showed a trend towards regulation with increased expression in lactating animals(1.34 -fold, p = 0.05) as was observed in the independent samples (Figure5A). These results indicate that the protein lists generated with mass spectrometry do not accurately reflect the true protein content of the samples analyzed.

Figure 4 :

 4 Figure 4: Immunoblot validation of mass spectrometry results. Shown are immunoblots of independent replicates for proteins that were significantly regulated with depicted FDR ≤ 22% in mass spectrometry. Representative immunoblots with approximate molecular weights and loading controls appear below the corresponding bar graph quantification for A) Depicted astrocyte-enriched proteins. Aqp4 was down-regulated by hyperosmosis with immunoblot analysis. B) Depicted high-fold change protein Ezrin and low FDR protein Ptprf. Ezrin expression was significantly down-regulated in lactating animals. C) Depicted proteins significantly regulated only in the hyperosmotic condition. Independent samples confirmed a significant up-regulation for Ndrg2 in both hyperosmotic and lactating rats. Bar graphs depict the mean log2 expression ratio relative to virgin samples ±SEM. * p ≤ 0.05.

Figure 5 :

 5 Figure5: Trouble shooting: Immunoblot analysis of mass spectrometry samples and SON plasticity markers analyzed using independent replicates. Representative immunoblots with approximate molecular weights and loading controls appear below the corresponding bar graph quantification. A) Expression levels of significantly regulated proteins were evaluated on the same samples used for mass spectrometry analysis. Protein regulations found by mass spectrometry were not confirmed, however, Nrdg2 showed a trend towards up-regulation in lactation (F(2,9) = 2.99, p = 0.10, post-hoc analysis V-L p = .05). B) Based on literature, expression levels of Pdia3 and TH were expected to be upregulated in both plasticity conditions. TH was significantly down-regulated by plasticity compared with virgin animals. Bar graphs depict the mean log2 expression ratio relative to virgin samples ±SEM. * p ≤ 0.05.

  in a matter of minutes[231][232][233]. Acute brain slices exposed to hyposmotic medium showed a dramatic increase in GFAP proteins levels in the SON already at 5 minutes after exposure, which had returned to normal after 20 minutes[233]. The same pattern was observed in rats given i.p. water injections to reduce osmolality, with GFAP levels peaking after 10 minutes and returning to baseline after30 minutes [233]. Chronically dehydrated rodents show a decrease in GFAP expression, which returns to baseline upon rehydration[234], indicating expression level differences occur between different models of SON plasticity, but that these changes can happen rapidly.

  3) was used to perform a hypergeometric test to assess over-representation of GO terms for biological process. Statistically significant (p ≤ 0.005 after correction with Benjamini and Hochberg False discovery Rate) over-representation of GO terms was derived from comparison with the builtin reference set of all annotated rat genes.ImmunoblottingMass spectrometry immunoblotting validations were performed on 5 independent replicates of P2+M from virgin, lactating, and hyperosmotic animals. Proteins were diluted in SDS sample buffer and denatured by heating to 98° C for 5 min. Five micrograms of each sample were separated on Criterion 4-15% TGX Stain-Free precast gradient polyacrylamide gels (BioRad) and transferred to PVDF membrane (BioRad). Membranes were blocked in 5% skim milk-Tris-buffered saline/ 0.05% Tween 20 (TBS-T) and incubated overnight with primary antibody at 4 °C. Primary antibodies used were against Ezrin (1:1,000, GenScript), Ndrg2(1:200, Santa Cruz), Aldoc (1:1,000, Santa Cruz), Atp1a2 (1:5,000, Millipore), Ppap2b (1:1,000, a kind gift from Dr. Andrew Morris, University of Kentucky, USA), Aqp4 (1:200, Abcam), Intersectin1 (1:500, a kind gift from Dr. Oleg Shupaliakov, Karolinska Institute, Sweden), Slc6a11 (1:500, Abcam) Ptprf(1:200, Neuromab), PDI(1:500, Santa Cruz), NSF (1:500, Genscript), Pdia3 (1:500, Santa Cruz), and TH (1:500, Millipore).Probed membranes were incubated with SuperSignal West Femto Chemiluminescent substrate (Thermo-Scientific) and the reaction was imaged with a Li-Cor Odyssesy 2800 scanner. Blots were quantified using ImageStudio with background correction. To visualize initial protein loading for quantification normalization, gels were activated with UV-light prior to transfer using Gel Doc EZ Imager. ImageLab was used to quantify the optical density of the total protein content of each sample on the gel which was used as a loading control for the corresponding immunoblot.Statistical analyses were performed with SPSS 21 (IBM). Immunoblot validations were analyzed by one-way ANOVA and subsequent post-hoc analysis was performed with Fisher's least significant difference (LSD) test.ABSTRACTA change in efficacy of hippocampal synapses is critical for memory formation. So far, the molecular analysis of synapses during learning has focused on small groups of proteins, whereas the dynamic global changes at these synapses have remained unknown. Here, we analyzed the temporal changes of the hippocampal synaptic membrane proteome one and four hours after contextual fear learning, comparing two groups; 1) a fear memory forming 'delayed-shock' group and 2) a memory deficit 'immediate shock' group. No changes in protein expression were observed one hour after conditioning between the two experimental groups. However, 423 proteins were significantly regulated four hours later, of which 164 proteins showed a temporal regulation after a delayed shock and 273 proteins after the stress of an immediate shock. From the proteins that were differentially regulated between the delayed and immediate shock groups at four hours, 48 proteins, most prominently representing endocytosis, (amphiphysin, dynamin, synaptojanin1), glutamate signaling (glutamate [NMDA] receptor subunit epsilon-1, disks large homolog 3) and neurotransmitter metabolism (excitatory amino acid transporter 1, excitatory amino acid transporter 2, sodium-and chloride-dependent GABA transporter 3) were regulated in both protocols, but in opposite directions, pointing towards an interaction of learning and stress. Taken together, this dataset yields novel insight into diverse and dynamic changes that take place at hippocampal synapses over the time-course of contextual fear-memory learning.INTRODUCTIONThe hippocampus of the brain is involved in memory processing and the regulation of negative emotion, in particular the control of contextual fear[241]. Contextual fear learning is a form of associative aversive learning, wherein a delayed foot shock (Unconditioned Stimulus, US) that is paired with a conditioning chamber (Conditioned Stimulus, CS) elicits detectable fear responses in rodents[242]. However, a foot shock given immediately upon placement in the conditioning chamber results in an immediate shock deficit, wherein the contextual cues are not associated with the aversive stimulus[243]. This deficit has been attributed to failures in both and, the molecular mechanisms of which have remained elusive[245].The formation of lasting and stable neural representations of both the context and the aversive stimulus is dependent on the synthesis of new proteins[245][246][247][248], resulting in longlasting changes in synaptic plasticity and efficacy in key neural connections within the hippocampus. Over the years some individual genes, proteins and pathways have been identified that are involved in the temporal process of memory consolidation from a transient newly learned short-term memory (STM) to a stable and lasting long-term memory (LTM) fear memory[249]. Depending on the type of conditioning protocol, consolidation of aversive memories has been shown to depend on either one or two phases of protein translation[250, 251]. The first phase occurs early, i.e., within the first hour, and has been interpreted as being one in which transcription factors and immediate early genes are being expressed[250, 251].The later phase, several hours after conditioning, is presumed to underlie synaptic remodeling required for long-term memory formation during which structural and plasticity genes are translated and their protein products trafficking into synaptic structures[250, 251]. For contextual fear conditioning, wherein the conditioning context is paired with a single delayed 0.7 mA foot shock, two sensitive windows of protein synthesis have been identified, one at the time of conditioning, and the second 4 h later[252]. Thus far, the focus has been on identifying the underpinnings of glutamate receptor plasticity and its role in memory formation[249, 253]. By using pharmacological, genetic and molecular investigation, various targets have been identified[247, 254]. Overall, these studies suggested that a cascade of molecular and cellular events results in a durable form of synaptic plasticity and modification[247, 253, 255]. Moreover, both somatic and local dendritic production of new proteins including specific transcription factors, synaptic structural components, receptors and adhesion molecules have been shown to be important for hippocampal memory-associated synaptic plasticity[255][256][257][258]. However, a profile of global protein changes at the hippocampal fear-conditioned synapse over the time-course of consolidation has remained elusive.

Figure 1 .

 1 Figure 1. Changes in the hippocampal synaptic membrane proteome composition 1 h and 4 h after fear conditioning. A) Experimental setup of proteomic analysis of hippocampal synaptic membrane fractions after fear conditioning (see Fig. S2). Four different comparisons were made, indicated by different colors; delayed vs. immediate shock at 1 h (green) and at 4 h (purple), and regulation over time in the delayed shock group (red) or the immediate shock group (blue). B) Venn diagram displaying overlap in regulation after FDR analysis for the different comparisons. C) Proteins that were significantly up (red) or down regulated (blue) for the delayed vs. immediate shock at 4 h (DS4h-IS4h) were differentially enriched over 19 functional groups, with most regulation observed in 5 groups (bold). D) Representative expression changes (DS4h-IS4h; log2) as measured by iTRAQ of individual proteins belonging to the 5 most significantly regulated functional groups. Bar graphs show mean±SEM.

Figure 2 .

 2 Figure 2. Validation of iTRAQ by immunoblot analysis. Bar graphs (mean±SEM) with expression changes (DS4h-IS4h; log2) as measured by iTRAQ (black) and immunoblot (light gray) of individual proteins from several group, such as cell adhesion and intracellular signal transduction, cell metabolism, endocytosis and exocytosis, LGIC signaling, neurotransmitter metabolism and transport and protein clustering that were concomitantly regulated in the same direction (TableS2). Upper panels display representative examples of protein expression in the DS4h (DS) and IS4h (IS) samples.

Fig

  Fig. S5 in the Supporting Information).

Figure 3 .

 3 Figure 3. Temporal changes in hippocampal synaptic membrane protein composition induced by delayed shock. A) Experimental setup (see Fig. S2) with the regulation over time in the delayed shock group (highlighted). B) FDR analysis revealed 164 proteins (87 up regulated, and 77 down regulated) as being significantly regulated over time after a delayed shock. Proteins that were significantly up or down regulated were differentially enriched over 15 functional groups, with most regulation observed in 5 groups (bold). The peptide/Neurothrophin signaling group was too small (0 members), and hence is indicated in grey C) Representative expression changes (DS4h-DS1h, log2) as measured by ITRAQ of individual proteins belonging to the 5 most significantly regulated functional groups. D) Expression changes of non-mitochondrial proteins (DS4h-DS1h, dark grey; log2) of individual proteins significantly regulated by the delayed shock only, and showing a large expression difference when compared with temporal regulation by the immediate shock (IS4h-IS1h, light grey). Bar graphs show mean±SEM.

Figure 4 .

 4 Figure 4. Temporal changes in hippocampal synaptic membrane protein composition induced by immediate shock. A) Experimental setup (see Fig.S2) with regulation over time in the immediate shock group (highlighted). B) FDR analysis revealed 273 proteins (92 up regulated, and 181 down regulated) as being significantly regulated over time after an immediate shock. Proteins that were significantly up or down regulated were differentially enriched over 19 functional groups, with most regulation observed in 5 functional groups (bold). The peptide/Neurothrophin signaling group was too small (1 member), and hence is indicated in grey. C) Representative expression changes (IS4h-IS1h, dark grey; log2) as measured by ITRAQ of individual proteins belonging to the 5 most significantly regulated functional groups. D) Representative expression changes (log2) from 5 functional groups, as measured by the immediate shock only, and showing a large expression difference when compared with the temporal regulation by the delayed shock (DS4h-DS1h, light grey). Bar graphs show mean±SEM.

(

  32%), each with 8 proteins up regulated in the DS4h-DS1h comparison and down regulated in the IS4h-IS1h comparison, from which examples are shown (Fig. 3C, Fig. 4C, example; Fig. S6 in the Supporting Information).

Figure 5 .

 5 Figure 5. Validation of delayed and immediate shock by immunoblotting. A) Experimental setup with no shock (no fear learning), the delayed shock (DS; fear learning) and immediate shock (IS, stress) groups analyzed 4 h after their respective training. B) Representative expression changes (mean±SEM vs. no shock group, log2) is indicated for selected proteins from the group of endocytosis/exocytosis, glutamate receptor subunits, neurotransmitter metabolism and scaffolding (DS 4 h, dark grey; IS 4 h, light gray), with significant (*p<0.05) and trend ( # p<0.2) indicated.

  -transformed and normalized by variance stabilizing normalization implemented in the VSN R-package[278].Normalized log-transformed iTRAQ reporter intensities of each spectrum were centered to the average intensity of all iTRAQ reporter ions in the respective spectrum to obtain standardized values. Sample-level protein abundance was determined by taking the average centered iTRAQ reporter intensities of the respective iTRAQ reporters of all spectra assigned to that protein. Statistical evaluation of protein abundance differences was performed with the SAM R-package.Supplemental Figure S2. Hippocampal synaptic membrane proteome after fear conditioning. A)Experimental setup of proteomic analysis of hippocampal synaptic membrane fractions after fear conditioning. Delayed and immediate shock groups were analyzed 1 h and 4 h after conditioning. B) Retrieval was carried out 24 h after conditioning in the same context. During retrieval, only animals that received a delayed shock at the end of the training exhibited a fear response, whereas animals that received an immediate shock during conditioning exhibit an immediate shock deficit and no fear response. Data points show mean±SEM. C) Using a strict selection criteria of 2 or more quantifiable peptides in all 6 biological independent samples (n=3 animals per sample; n=6 samples) of the three 8-plex replicates, we identified a total of 724 proteins that were assigned to 19 different functional groups.Supplemental FigureS3. Functional group regulation. Proteins were divided over 19 different functional classes that were previously defined[START_REF] Wenzel | The influence of long-term potentiation on the spatial relationship between astrocyte processes and potentiated synapses in the dentate gyrus neuropil of rat brain[END_REF]. For the three different comparisons (DS4h-IS4h, DS4h-DS1h, and IS4h-IS1h), the % of each class that showed significant regulation (FDR) is shown. The five most regulated classes (six for DS4hIS4h) are given in bold. Note that each class contained at least 9 proteins, except 'peptide/Neurotrophin signaling' that contained only 2 proteins (bold, gray). Supplemental FigureS4. Correlation of iTRAQ and immunoblot analysis. Shown is the correlation (r 2 =0.439; P=0.022) between the values obtained from iTRAQ and immunoblot analysis from 27 proteins analyzed. See Table

[

  129]. The trafficking of existing calcium permeable GluA2-lacking AMPA receptors from extra synaptic sites, and eventual insertion of new GluA-2 containing AMPA receptors into the postsynaptic membrane, strengthen the transmission of activated synapses [291-296]. The overall success of synaptic memory consolidation is dependent upon de novo protein synthesis. Application of either mRNA or protein synthesis inhibitors during one or two time windows, namely immediately around the period of acquisition and/or from 3 to 6 hours after memory acquisition, can disrupt long-term consolidation [251, 297]. The requirement of either one or two phases of consolidation appears to vary by training protocol, but both are PKA-dependent [252].Systems consolidation, the second phase of memory consolidation, starts after closure of the window of synaptic consolidation, at 6 hours, and involves larger scale changes throughout the brain to support formation of a long-term memory[298, 299]. During this period, the newly formed memory is integrated into the cortical network as various components of the memory trace are reorganized between the hippocampus and the cortex[299][300][301]. After the completion of systems consolidation the neural representation of the memory mainly resides in the cortex and remains largely unaffected by hippocampal lesions[302], but can be impaired as the result of cortical lesions

amino acid transporter 2 (

 2  that resulted in overstimulation of NMDA receptors due to abnormally high glutamate levels[315]. Interleukin 1 receptor (IL1-R) knockout mice have a variety of synaptic and behavioral impairments, but memory performance in a Morris water maze and during contextual fear conditioning can be restored to wild type levels by the expression of IL1R specifically in astrocytes[316]. Mice with decreased levels of the perisynaptic astrocyte Na + /K + transporting ATPase alpha 2 (Atp1a2) also have spatial memory impairments as evidenced by poor performance on the Morris water maze[142].

  ). A second source of information came from an iTRAQ-based proteomics data set([320], Chapter 5) evaluating protein expression level changes in mouse hippocampal synaptic membranes 1 and 4 hours after fear conditioning training. Since biochemically isolated synaptic membrane fractions, like synaptosome fractions, contain a certain amount of perisynaptic astrocyte proteins, possibly derived from astrocyte processes linked to pre-and post-synaptic terminals (unpublished observations/Chapter 3), this data set can be used to include astrocyte proteins likely associated to the synapse. Proteins significantly regulated (FDR ≤ 10%) by a delayed shock (DS) vs. an immediate shock (IS) after 4 hours of consolidation or in a temporal fashion in the DS group between 1 and 4 hours after training were selected, giving 565 proteins that met this criteria (Figure1A). Third, because a large body of literature has been dedicated to the elucidation of transcripts and proteins involved in memory-related processes, we next conducted a literature search to uncover additional potential target proteins. The results of 10 published microarray and proteomic studies that detected significant gene or protein regulations after a hippocampal-based memory paradigm were compiled[321][322][323][324][325][326][327][328][329][330]

  Because the iTRAQ proteomic analysis([320], Chapter 5) most closely resembled the set-up of our own fear memory consolidation time-series, proteins regulated in this paradigm were deemed the most relevant to our analysis and thus given high priority during the final target selection phase. Consideration of this information, combined with the relevance of that candidate's biological function, degree of astrocyte enrichment, fold change in SON, and antibody availability, resulted in the selection of a total of 15 astrocyte-enriched proteins for subsequent immunoblot analysis. Of the selected proteins, aldolase C (Aldoc), Na + /K + ATPase alpha-2 subunit (Atp1a2), GABA transporter 3 (GAT3), and lipid phosphate phosphohydrolase 3 (Ppap2b) were regulated by consolidation and in the SON; Excitatory amino acid transporter 1 (GLAST), excitatory amino acid transporter 2 (GLT-1), enoyl-CoA hydratase (Echs1), ezrin (Ezr), and basigin (Bsg) were regulated by consolidation; Glyoxylase 1 (Glo1), alpha-enolase (Eno1) and glutamine synthetase (GS) regulated by consolidation and in selected literature studies; N-myc down-stream-regulated gene 2 (Ndrg2) and MLC1 homolog (MLC1), were regulated in the SON; Glial fibrillary acidic protein (GFAP) was regulated in selected literature studies.Expression levels of the selected proteins were evaluated on dorsal hippocampal synaptosome samples from contextually fear conditioned mice. Mice were exposed to a conditioning chamber with either no shock (NS), an immediate shock (IS), which does not allow for the associative memory formation between context and shock [331], or a delayed shock which does illicit the formation of a fear memory specific to the context in which the shock was received[242]. Immunoblots for astrocyte proteins were performed at selected time points from a time-series of samples collected 4, 7, 24, 48, and 72 hours after fear conditioning training. The 4 hour time-point represents the protein-synthesis dependent phase of synaptic consolidation, whereas all other time-points were during the phase of systems consolidation, which is protein synthesis-independent[251]. All proteins were evaluated at the 24 hour time point. Proteins that were regulated at 24 hours were evaluated over the entire time course. However, proteins not regulated at 24 hours are not likely to be regulated at 48 or 72 hours, hence these time points were then not evaluated, whereas these proteins may still be regulated at earlier time points and thus were evaluated at the 4 and 7 hour points.

Figure 1 .

 1 Figure 1. Selection of candidate astrocyte proteins involved in memory consolidation. Venn diagram illustrates the total protein numbers for each of the selection criteria and the number of proteins that overlap between criteria. Criteria include proteins regulated by hyperosmosis or lactation in the supraoptic nucleus (22% FDR, Chapter 4), astrocyte-enriched proteins [191], proteins regulated by contextual fear memory consolidation (10% FDR) in DS vs. IS at 4 h after training or temporally between 1 and 4 h in an iTRAQ proteomics analysis (Chapter 5), and candidates identified as regulated by memory consolidation in a literature screen of proteomics and microarray studies. Candidate proteins selected for further evaluation are indicated.

Figure 2 .

 2 Figure 2. Proteins not regulated by fear memory consolidation. Shown are immunoblots and loading controls for indicated proteins with n = 5 per sample, per time point analyzed by ANOVA. Bar graphs represent mean + SEM.

Figure 3

 3 Figure 3. Ppap2b is regulated only during synaptic consolidation. Shown are immunoblots and loading controls for indicated proteins with n = 5 per sample, per time point analyzed by ANOVA with Fischer LSD post hoc testing. 4h: F(2,12) = 14.77, p = 0.001; DS vs. NS was -2.43-fold, p = 0.0002; DS vs. IS was -1.80-fold, p = 0.004. Bar graphs represent mean + SEM. ** = p ≤ 0.005, *** = p ≤ 0.0005.

Figure 4 .

 4 Figure 4. Proteins significantly regulated during both synaptic (4 h) and system (7-72 h) level consolidation. Shown are immunoblots and loading controls for indicated proteins with n = 5 per sample, per time point analyzed by ANOVA with Fischer LSD post hoc testing. A) Atp1a2: 4 h: F (2,12) = 6.11, p = 0.02, DS v NS was -1.21-fold, p = 0.006, DS vs. IS was -2.15-fold, p = 0.03; 7 h: F(2,12) = 9.21, p = 0.004, DS vs. NS was -2.38-fold, p = 0.002, DS vs. IS was -2.10-fold , p = 0.004 ; 24 h: F (2,12) = 7.76, p = 0.007, DS vs. NS was -1.68-fold, p = 0.005, IS vs. NS was -1.67fold, p = 0.005. B) Ndrg2: 4 h: F(2,12) = 4.62, p = 0.03, DS vs. NS was -1.18-fold, p = 0.02, DS vs. IS was -1.18-fold, p = 0.02 ; 24 h: F(2,12) = 59.52, p <0.001, IS vs. NS was -3.21-fold, p <0.001, DS vs. NS was -3.34-fold, p <0.001; 48 h: F(2,11) = 6.85, p = 0.01, DS vs. NS was 1.25-fold, p = 0.01, DS vs. IS was 1.26-fold, p = 0.006. (Legend continued on next page)

Figure 5 :

 5 Figure 5: Proteins significantly regulated only during system consolidation. Shown are immunoblots and loading controls for indicated proteins with n = 5 per sample, per time point analyzed by ANOVA with Fischer LSD post hoc testing. A) GAT3: 24 h: F(2,11) = 5.01, p = 0.03, IS vs. NS was -1.18-fold, p = 0.03 , DS vs. NS was -1.53-fold, p= 0.01. B) Echs1: 24 h: F(2,12) = 65.21, p <0.001, IS vs. NS was -3.72fold, p <0.001, DS vs. NS was -3.60-fold, p <0.001; 72 h F(2,12) = 21.16, p <0.001, IS vs. NS was -3.06-fold, p = 0.001, DS vs. NS was -4.35-fold, p <0.001. C) GS: 24 h: F (2,12) = 4.85, p = 0.03, IS vs. NS was -1.29-fold, p = 0.03, DS vs. NS was -1.33-fold, p = 0.02; 72 h: F(2,12) = 11.04, p = 0.002, IS vs. NS was 10.19-fold, p = 0.001, DS vs. NS was 3.29-fold, p = 0.04, IS vs. DS was 3.10-fold, p = 0.04. (Legend continued on next page).

-Figure 6 :

 6 Figure 6: Global protein expression profiles per time point. Cross plots represent an overview of the log2 expression ratio between IS vs. NS and DS vs. NS as measured by immunoblot for all evaluated proteins at the indicated time point. Each dot represents a single protein.All proteins evaluated by immunoblot were included, regardless of whether significant regulations were detected. At 4 h, the 4 dots most decreased by DS represent Ppap2b, GLAST, Atp1a2, and Ezr.

Figure 7 :

 7 Figure 7: Analysis of consolidation of fear memory and short term memory of MLC1 null mice. A) Baseline behavioral parameters of MLC1 null mice (n = 10) and wild type (WT)(n = 15) mice were automatically measured in the contextual fear conditioning box during the 180 s prior to receiving the shock. Percent of the box explored, percent of time spent inactive, total distance moved (cm), and pre-shock freezing percent were not different between WT and null mice. B) During a 180-s memory retention test 24 h after training, MLC1 null mice show impaired fear memory when tested in the original conditioning chamber (context A), as assessed by percent of the total test session spent freezing (context: F(1,24) = 26.70, p = 0.00003, context*genotype: F(1,24) = 0.94, p = 0.34 ; post hoc context A: -1.54-fold, p = 0.02). This behavior did not generalize to a novel chamber (context B) tested 2 h later as no significant freezing differences were found in context B (post hoc context B, p = 0.38). C) Assessment of working memory using a T-maze showed a trend (p = 0.09) towards impairment in MLC1 null mice as determined by percent of successful alternations with no difference in side bias. All bar graphs depict the mean +SEM. * = p ≤ 0.05

1 :

 1 Immunoblot validation of iTRAQ mass spectrometry regulations 4 h after contextual fear memory consolidation. All iTRAQ regulations had an FDR ≤10%. Shown are immunoblot quantifications based on n= 3-5 independent samples per condition. Bar graphs represent mean + SEM.

Figure 1 :

 1 Figure 1: Schematic depiction of 4 isolation methods.A) Percoll-isolated gliosomes and synaptosomes, B) Pellet 2 + microsomes (P2+M), C) Sucrose-synaptosomes, D) Sucrose-synaptic membranes. Note that the P2 generated during the isolation of gliosomes (A) is not the same as the P2 in the P2 + M isolation protocol (B).

  analyse van genen die in astrocyten worden geïnduceerd door de aanwezigheid van neuronen. Deze methode bestaat uit het pipeteren van ijskoude PBS op neuron-astrocyt cokweken, wat resulteert in het loslaten van de neuronen omdat deze andere adhesievermogens hebben dan astrocyten. Met deze methode is het mogelijk om astrocyten te isoleren uit deze co-kweek met 90% zuiverheid. Vervolgens zijn met behulp van microarray-analyse de transcriptionele veranderingen in astrocyten geinduceerd door neuronen bepaalt. Gevonden werd dat met name genen met een metabole functie werden gereguleerd, waaronder de afname van mRNAs voor Insig1, Hmgcs, en Dhcr7, die een rol spelen in lipidenmetabolisme.Omdat transcriptionele veranderingen niet strikt correleren met veranderingen op eiwitniveau, en dus biologische functie, heb ik tevens biochemische isolatie-methodes geëvalueerd op hun geschiktheid voor analyse van astrocyt-eiwitten. Gliosomen, geïsoleerd met behulp van een Percoll°-gradiënt, zijn verrijkt voor astrocyt-eiwitten, waaronder PAPeiwitten, en daarmee mogelijk een geschikt preparaat voor de identificatie van nieuwe PAPeiwitten en meten van veranderingen in PAP-eiwitniveaus gedurende experimentele veranderingen in synaptische plasticiteit. In hoofdstuk 3 heb ik met behulp van label-vrije TripleTOF massa spectrometrie een meer gedetailleerde analyse gedaan van de eiwitten in gliosomen, en dit vergeleken met Percoll°-geïsoleerde synaptosomen. Gevonden werd dat gliosomen verrijkt zijn voor G-eiwit gemedieerde signaaleiwitten, terwijl synaptosomen verrijkt zijn voor vesicle-gemedieerd transporteiwitten. Verrassend was verder de observatie dat niet alleen gliosomen verrijkt zijn in PAP-eiwitten, maar dat synaptosomen zelfs nog meer verrijkt zijn voor PAP-eiwitten.Na een geschikte methode voor analyse van PAP eiwitten te hebben gevonden, ben ik verder gegaan met het bepalen van de bijdrage van astrocyten in synaptische plasticiteit in vivo. Het is bekend dat de supra-optische nucleus (SON) functionele en structurele neuron-glia plasticiteitsveranderingen ondergaat als gevolg van lactatie en hyperosmose. Tijdens deze fysiologische stimuli ondergaat de astrocyt morfologische veranderingen; het trekt zijn uitlopers (PAPs) terug van synapsen op oxytocin magnocellulaire neuronen, waarbij de diffusie en beschikbaarheid van gliotransmitters in de synaps-spleet veranderd. Deze structurele plasticiteit resulteert in een verandering in synaptische plasticiteit die van belang is bij the fysiologische reactie op hyperosmose en lactatie.In hoofdstuk 4 heb ik vervolgens Orbitrap label-vrij massa spectrometrie gebruikt voor kwantitatieve analyse van eiwitexpressie in maagdelijke, lacterende, en hyperosmotische ratten om de eiwitten te identificeren die betrokken zijn bij het moleculaire mechanisme achter deze plasticiteitsveranderingen. Ik heb 985 eiwitten gevonden die gereguleerd werden door plasticiteit, en door het vervolgens filteren van de resultaten heb ik dit aantal teruggebracht tot 86 eiwitten die vermoedelijk in astrocyten een rol spelen bij de plasticiteitsveranderingen. Helaas hebben we deze resultaten met Western blotting voor de meeste eiwitten niet kunnen valideren, al werd voor N-myc down-stream regulated gene 2 (Ndrg2) wel een significante verhoging in eiwitniveaus gevonden bij hyperosmose en lactatie.Ik concludeer dat er waarschijnlijk te veel bronnen van zowel biologische als technische variatie in onze experimentele opzet aanwezig was om de massa spectometrie-data te kunnen valideren, en bediscussieer in hoofdstuk 4 enkele verbeteringen in de methodiek die bij vervolgexperimenten een succesvollere analyse mogelijk maken.Daarnaast hebben we contextuele angst conditionering gebruikt als model voor synaptische plasticiteit, waarbij muizen eerst een conditioneringskamer kunnen onderzoeken voordat ze een voetschok krijgen en daardoor een associatie maken tussen de voetschok en de context (vertraagde schok). Interessant hierbij is dat muizen die een voetschok krijgen voordat ze de conditioneringskamer kunnen onderzoeken (onmiddellijke schok), geen associatie maken tussen de schok en de context, deze muizen kunnen daarmee fungeren als controle voor de stressvolle ervaring van een voetschok, waarmee het effect van synaptische plasticiteit specifiek betrokken bij opslaan van een angstherinnering kan worden bepaald. Daarmee hebben we in hoofdstuk 5 bepaald dat 4 uur na training van de muizen er in de hippocampus 423 eiwitten significant worden gereguleerd, waarvan 164 door een vertraagde schok en 273 door een onmiddellijke schok. Regulatie van meerdere astrocyt-eiwitten werd gevonden, waaronder een verlaging van de glutamaat transport-eiwitten GLAST en GLT-1, en GABA transport-eiwit Scl6a11/GAT3 op 4 uur na een vertraagde schok. Als geheel laat deze analyse zien dat een onmiddellijke schok in staat is om grote veranderingen in eiwitniveaus te veroorzaken, en tevens dat astrocyt-eiwitten gereguleerd worden door zowel een onmiddellijke schok als ook door specifiek het opslaan van angstherinneringen.

  figent moins que les animaux contrôles lors des tests de mémoire de peur contextuelle ce qui

  you shared with me and helping me to get situated into the lab. Most importantly, thank you for making nice thesis books that I could use for inspiration! Andrea, I have not seen a book yet but I know it is on the way. Thank you for showing me the ropes in my first months being part of team glia and for always being so concerned about the state of my struggles with this PhD. I enjoyed all the times we were roommates, both during congresses and while in Amsterdam! Thank you to all my former officemates who had to tolerate my constant emotional breakdowns and desire for silence. Jörg, our many late night conversations about life, science, and the lab have been very important to me. You have always been good at making me smile by cracking a joke at someone else's expense. Anya, thank you for being a part of team "quiet office". I look forward to having our PhD celebration together! Rameshy, I was so sad to see you go, but you will always have a special place in my heart. Thank you for always being so sweet and caring and dropping everything to take breaks in the hall with me when I needed to vent or collapse into tears. I will also never forget all the delicious Indian meals and the soreness I felt after the first time you taught me to play badminton! To the four Dutchy girls, Anne-Lieke, Céline, Leanne, and Esther, it has been a true pleasure getting to know you. Thank you for all the moments when you have helped and supported me as friends and colleagues. Anne-Lieke, make the best of your new found brown-nosing techniques! Céline, thanks for allowing me to vent my frustrations to you and for your expert behavioral analyses of what constitutes autism spectrum disorders. Leanne, I appreciate all your help with the fear conditioning and DEC organization. Be careful that you don't end up like Danai, being so valuable that they never let you leave! Esther, if science fails you I am confident you are in a great location to pursue your talents in supplying videos to the erotic cat fetish market.To the party gang, Natasha, Danai, Loek, Bart, and Priyanka, you guys have really been rocks for me throughout the past 4 years. I thank you for all the holidays, advice, pot luck dinners, new experiences, and nights I can't remember. My memories with you are the ones I cherish the most. Natasha, my bestie, thank you for putting up with the years of epic drama, introducing me to delicious Indonesian food and delicious Kim Cesarion, and always being my partner in crime. Danai, thanks for all the "breaks", delicious dinners, and always checking in on me to make sure I am ok. Loek, thank you for being my superman, whether it is fixing my broken bicycle, carrying heavy objects, or giving me shoulder to cry on when I need it. An extra thanks to Danai and Loek for accepting the paranymph responsibilities and helping me

  home. Thank you to my students Lonneke and Jytte for helping me with experiments and making me feel useful! To my colleagues outside the MCN-Margherita, thank you for being my safe haven during all the ENC Network meetings. I could not have survived them without you! Mohit, thanks for all the weekend coffee breaks and MLC1 discussions. David, thank you for spending so much time teaching me how to do in-gel digestions.Although the majority of my research was conducted in Amsterdam, I would be remiss not to acknowledge the help I received from my collaborators. At my "home" institution in Bordeaux, thank you Jean Marc for being my temporary supervisor, introducing me to the SON, and helping me with all my administrative and government paperwork. Thank you Aurélie, Nathalie, and Jérôme for teaching me how to work in the lab. A special thanks to Jérôme for also finding me a place to live and helping me to move to Amsterdam. Philippe, I appreciate your detailed responses to my SON questions and sharing your antibodies with me. Ciaran, thank you for being my English-speaking refuge and for all the nights drunk on Bordeaux wine.A large part of my successful thesis is owed to my Italian collaborators. Gianni, thank you for this opportunity to collaborate and for accepting the position on my thesis committee. Marco, I have really enjoyed all our e-mail conversations. I think we have both taught each other over these many iterations of sample preparation and analysis, so I am grateful for your participation in this project and patience with the slow nature of mass spectrometry analysis. I would not have made it to this point had it not been for the friends and mentors who inspired and supported me along the way, so thank you to Dr. Densie Flaherty for initiating my passion for research and neuroscience, to the N-track for making Europe feel safe, and to Dr. Kelly Miller for believing in me and living the life I (still) dream of having. To all of you, I wish you the absolute best success and happiness, and I promise to try to find the same for myself. And, after six years of living in Europe, I am now almost comfortable giving you all kisses XXX Thank you to Luna and Tuna for your moral support during the thesis writing process. Although you have been very critical of my work, it was my pleasure to provide you with mountains of papers to play with and an over-heated laptop to sit on.

Table 1 . Gene ontology-overrepresentation analysis for biological processes for all mRNAs (394) regulated in astrocytes when co-cultured with neurons GO ID GO Term p-value GO:0016125

 1

		sterol metabolic process	2.20E-10
	GO:0006991	response to sterol depletion	4.60E-07
	GO:0031667	response to nutrient levels	0.00019
	GO:0045332	phospholipid translocation	0.00049
	GO:0015679	plasma membrane copper ion transport	0.00082
	GO:0000245	spliceosome assembly	0.00089
	GO:0001955	blood vessel maturation	0.00288
	GO:0007218	neuropeptide signaling pathway	0.00291
	GO:0006800	oxygen and reactive oxygen species	0.00314
		metabolism
	GO:0006968	cellular defense response	0.00409

Table 2 . Gene ontology-overrepresentation analysis for biological processes for mRNAs (270) up-regulated in astrocytes when co-cultured with neurons Gene Ontology ID Gene Ontology Term p-value GO:0007584

 2

		response to nutrient	0.00013
	GO:0045332	phospholipid translocation	0.00019
	GO:0015679	plasma membrane copper ion transport	0.00032
	GO:0001955	blood vessel maturation	0.00114
	GO:0008406	gonad development	0.00132
	GO:0007218	neuropeptide signaling pathway	0.00324
	GO:0006911	phagocytosis, engulfment	0.00326
	GO:0007160	cell-matrix adhesion	0.00475

Table 3 . Gene ontology-overrepresentation analysis for biological processes for mRNAs (124) down-regulated in astrocytes when co-cultured with neurons Gene Ontology ID Gene Ontology Term p-value GO:0016125

 3

		sterol metabolic process	2.10E-11
	GO:0006991	response to sterol depletion	8.50E-09
	GO:0000245	spliceosome assembly	0.00055
	GO:0006481	C-terminal protein methylation	0.00342
	GO:0015819	lysine transport	0.00342
	GO:0046498	S-adenosylhomocysteine metabolic process	0.00382
	GO:0006776	vitamin A metabolic process	0.0047

Table 1 : TripleTOF detected and significantly enriched protein numbers

 1

	Footnotes:
	a) At least 1 unique peptide identified
	b) Detected in at least 3 of the 5 homogenate samples or 5 of the 8 synaptosome or gliosome
	samples
	c) 10% FDR

Table 2

 2

	, Supporting table 3), such as 'synaptic transmission' (e.g. NMDA-
	receptor subunits GRIN1/2b, GABA receptor subunit gamma, synaptophysin, SynGAP),
	'regulation of exocytosis' (e.g. Vamp2, syntaxin 1A, synaptotagmin 1, Rab3A) and 'ion
	transmembrane transport' (e.g. sodium-potassium transporters Atp1b2, Atp1b3, voltage-
	gated calcium channel subunits B1/B2, V-Atpase subunits A1/B2/C1/E1 for acidification of
	vesicles). Accordingly, the synaptosome proteome was enriched for the molecular function
	'glutamate receptor binding' (including post synaptic density scaffold proteins PSD95, Shank1,
	and Homer1) which is in line with enrichment for glutamatergic synapses (Table 2, Supporting

Table 2 : Over-represented biological processes and molecular functions in comparison with homogenate

 2

	GO-ID	p-value	Count	%	Description
	Gliosome versus Homogenate
	Biological process
	9261	1.95E-05	20	24.7	ribonucleotide catabolic process
	50793	9.40E-04	25	15.7	regulation of developmental process
	48869	1.90E-03	44	11.4	cellular developmental process
	7155	2.73E-03	22	15.5	cell adhesion
	6811	4.83E-02	24	11.4	ion transport
	Molecular function
	3924	2.23E-03	17	21	GTPase activity
	5216	7.83E-03	17	18.1	ion channel activity
	19911	1.38E-02	3	100	structural constituent of myelin sheath
	5484	2.05E-02	4	50	SNAP receptor activity
	30507	2.05E-02	3	75	spectrin binding
	Synaptosome versus Homogenate
	Biological process
	7268	6.65E-06	31	28.7	synaptic transmission
	17157	6.65E-06	14	53.8	regulation of exocytosis
	34220	8.78E-06	27	30.0	ion transmembrane transport
					ribonucleoside triphosphate metabolic
	9199	7.77E-05	27	26.2	process
	51234	2.28E-04	116	13.6	establishment of localization
	Molecular function
	5215	4.37E-05	57	18.8	transporter activity
	17111	1.48E-03	39	17.7	nucleoside-triphosphatase activity
	5525	6.06E-03	29	18.2	GTP binding
	35254	1.10E-02	8	36.4	glutamate receptor binding
	19911	1.37E-02	3	100	structural constituent of myelin sheath
	4351	1.37E-02	3	100	glutamate decarboxylase activity
	149	1.37E-02	8	34.8	SNARE binding
	8289	3.41E-02	21	17.9	lipid binding

Table 3 : Over-represented biological processes and molecular functions in comparison with gliosomes or synaptosomes

 3

	GO-ID	p-value	Count	%	Description
	Gliosome versus Synaptosome
	Molecular function
	5102	3.63E-02	37	26.2	receptor binding
	3779	3.63E-02	36	24.8	actin binding
	4576	3.63E-02	5	83.3	oligosaccharyl transferase activity
	Synaptosome versus Gliosome
	Biological process
	6836	1.58E-04	20	40.0	neurotransmitter transport
	16192	4.87E-03	50	20.7	vesicle-mediated transport
	15986	2.00E-02	9	47.4	ATP synthesis coupled proton transport
	6000	2.00E-02	5	83.3	fructose metabolic process
	6812	2.42E-02	34	21.4	cation transport
	Molecular function
	5215	2.42E-02	56	18.5	transporter activity
	19200	2.84E-02	5	62.5	carbohydrate kinase activity
	16817	2.84E-02		18.4	hydrolase activity, acting on acid
			43		anhydrides
	4351	3.58E-02	3	100.0	glutamate decarboxylase activity

  Consortium [204] via the PRIDE partner repository with the dataset identifier PDX001375. KEC was sponsored by the Erasmus Mundus ENC Network, the University of Bordeaux and Vrije University. KWL, PvN and ABS received support from HEALTH-2009-2.1.2-1 EU-FP7 'SynSys' (#242167). SHRO is supported by Inserm, University of Bordeaux and La Fondation pour la Recherche Médicale (Equipe FRM).

Table 1 : Over-represented biological process terms in astrocyte-enriched plasticity cluster proteins

 1

	GO-ID	p-value Count	% Description	Genes in test set
	44281 2.32E-07	2.1 small molecule metabolic process PYCRL, ECH1, IMPA1, ACADS, ALDOC, ATP5B,
				SARS, CS, OGDHL, FDXR, ACOT1, ATP1A2,
				AMPD2, PSPH, PNPLA8, ACSS1, HMGCS2,
				PLCG1, ATG7, GPX4, AKR1B1, GCSH, AHCYL1,
				GK, ACSL6, PCCA, DUT
	51179 2.17E-04	1.3 localization	SLC38A3, SLC44A2, SLC6A1, AP2S1, ATP5B,
				TRAPPC2L, ASTN1, AP3S1, AQP4, ITSN1,
				UQCRQ, SRC, CD44, COPB1, ATG7, AGT,
				SLC4A4, ACSL6, RILP, SLC6A11, CKAP5, FDXR,
				SPIRE1, ATP1A2, DBI, ITGA6, PLCG1, RAB35,
				NTRK2, RAB22A, CDC42BPA, RHOT2
	51234 3.01E-04	1.3 establishment of localization	SLC38A3, SLC44A2, SLC6A1, AP2S1, ATP5B,
				TRAPPC2L, AP3S1, AQP4, ITSN1, UQCRQ,
				SRC, COPB1, ATG7, AGT, SLC4A4, ACSL6,
				RILP, SLC6A11, CKAP5, FDXR, SPIRE1,
				ATP1A2, DBI, PLCG1, RAB35, NTRK2,
				RAB22A, CDC42BPA, RHOT2
	6810	5.41E-04	1.3 transport	SLC38A3, SLC44A2, SLC6A1, AP2S1, ATP5B,
				TRAPPC2L, AP3S1, AQP4, ITSN1, UQCRQ,
				SRC, COPB1, ATG7, AGT, SLC4A4, ACSL6,
				RILP, SLC6A11, CKAP5, FDXR, SPIRE1,
				ATP1A2, DBI, PLCG1, RAB35, NTRK2,
				RAB22A, RHOT2
	19752 5.41E-04	2.5 carboxylic acid metabolic process	PYCRL, PNPLA8, ACSS1, ECH1, ACADS, SARS,
				ATG7, CS, GCSH, ACOT1, PSPH, ACSL6, PCCA
	43436 5.41E-04	2.5 oxoacid metabolic process	PYCRL, PNPLA8, ACSS1, ECH1, ACADS, SARS,
				ATG7, CS, GCSH, ACOT1, PSPH, ACSL6, PCCA
	6082	5.41E-04	2.5 organic acid metabolic process	PYCRL, PNPLA8, ACSS1, ECH1, ACADS, SARS,
				ATG7, CS, GCSH, ACOT1, PSPH, ACSL6, PCCA
	42180 5.59E-04	2.4 cellular ketone metabolic process	PYCRL, PNPLA8, ACSS1, ECH1, ACADS, SARS,
				ATG7, CS, GCSH, ACOT1, PSPH, ACSL6, PCCA
	9987	6.82E-04	0.7 cellular process	IMPA1, STAT5A, ATP5B, AP2S1, OGDHL,
				TRAPPC2L, PRDX4, GABBR1, AQP4, AP3S1,
				ACOT1, ITSN1, PSPH, UQCRQ, ACSS1, CD44,
				GPX4, COPB1, ATG7, KHDRBS1, AIFM1,
				ACADS, SARS, SPIRE1, PIGS, ST13, PNPLA8,
				PCCA, PYCRL, ECH1, EPDR1, ADCYAP1R1,
				ALDOC, ASTN1, SRC, SEC63, AGT, DMD,
				GCSH, NDRG2, PPAP2B, ACSL6, RILP, CKAP5,
				CS, FDXR, ATP1A2, AMPD2, DBI, ITGA6,
				HMGCS2, PLCG1, RAB35, AKR1B1, NTRK2,
				RAB22A, CDC42BPA, RHOT2, AHCYL1, GK,
				FABP7, DUT
	6629	2.02E-03	2.0 lipid metabolic process	IMPA1, ECH1, ACADS, ATP5B, FDXR, ACOT1,
				PIGS, DBI, PNPLA8, HMGCS2, PLCG1,
				PPAP2B, PCCA, ACSL6
	9991	2.91E-03	2.6 response to extracellular stimulus ITGA6, HMGCS2, CD44, ACADS, ATG7,
				STAT5A, PSPH, DBI, ACSL6, SRC
	16192 2.91E-03	2.4 vesicle-mediated transport	PLCG1, RAB35, COPB1, AP2S1, ATP5B,
				RAB22A, TRAPPC2L, AP3S1, SPIRE1, ITSN1,
				SRC

  Atpase alpha 2 subunit (Atp1a2). This discrepancy is conceivably due to the fact that the synaptosome preparation contains a mix of hippocampal synapses, not only the few synapses potentiated and recruited into the memory engram[260]. The expression levels of all astrocyte proteins using synaptosome preparations likely represent global changes in synaptic protein content. In early phases of consolidation, an astrocyte-mediate process known as heterosynaptic depression occurs, in which induction of LTP at one synapse results in induction of LTD in neighboring synapses in order to enhance specificity of the synaptic potentiation[174, 407]. As increased ensheathment of synapses is seen with LTP, reduced astrocytic coverage may be associated with LTD, and take place at the hippocampal synapses not potentiated by the fear memory. Although reduced astrocyte contact with synapses after LTD has not yet been formally demonstrated, it would be in line with our observation that the majority of synaptosomes at 4hr contain less PAP proteins. As an alternative explanation, the emerging evidence that PAPs may in fact retract, rather than extend, from the synapse during LTP[405, 406] would support our observation that PAP protein levels decrease in synaptosomes isolated from conditioned animals. Again, the exact nature of these PAP morphological discrepancies need to be resolved to allow solid conclusion about what types of changes are detected in our mass spectrometry experiments.Despite the appearance of astrocyte structural changes in acute slices only 30 minutes after stimulation, no significant protein regulations were found between DS and IS groups 1 hour after training using iTRAQ mass spectrometry (Chapter 5). This may suggest that effects of shock predominate at this early time point that all protein changes occur to similar extents in both conditions. Alternatively, changes specific to fear memory consolidation may be present at 1 hour after training but are still restricted to the few potentiated synapses, and are thus undetectable when synaptosomes from the entire dorsal hippocampus are analyzed.Consolidation of the fear memory and consolidation of the shock experience occur simultaneously and may involve different synapses, producing the possibility that true DS effects are masked by the noise of the IS effect. Therefore, the inclusion of a no-shock control is helpful in defining which regulations are likely the result of the shock and which are specific to fear memory consolidation. Although delineating the precise time-course of astrocyte

	monocarboxylate transporter 1 (MCT1), an astrocytic lactate transporter that provides
	neurons with metabolic fuel, is required for induction of LTP and long-term memory
	formation [409]. Bsg is an MCT1 chaperone protein that is required for optimal MCT1
	function, and thus could play important roles in maintaining energy supply to neurons during
	However, at 4 hours after training we saw decreased expression of known PAP proteins Ezrin, the synaptic scaling process [350, 351]. Because metabolic activity differs between different
	N-myc downstream-regulated gene 2 (Ndrg2), excitatory amino acid transporter 1 (GLAST), hippocampal sub-regions over the course of spatial memory acquisition and recall [410], it
	would be interesting to see if these large metabolic protein regulations can be attributed to a specific hippocampal region to give more insight into the molecular processes occurring at and Na + /K + protein regulations during the synaptic consolidation window could shed light on astrocytic this time point.
	contributions, it may be necessary to wait until later points after training to assess protein
	content. This may allow a substantial number of global regulatory mechanisms to take place
	or for acute stress effects in both groups to wear off before statistically significant
	consolidation specific protein changes can be detected.
	Later time points would also be of interest to investigate more thoroughly. Astrocyte
	metabolic functions, such as provision of lactate and supply of glutamine which can be
	converted by neurons into glutamate, are critical for synaptic function [17, 408]. At 72 hours
	after training, large regulations were observed in the IS, and to a lesser extent DS, group for
	proteins that are involved in metabolic functions, including basigin (Bsg), glutamine
	synthetase (GS), and enoyl-CoA hydratase 1(Echs1), indicating these proteins may serve
	important functions even at later time points. For example, proper expression of the

ACKNOWLEDGEMENTS K.C. was sponsored by the Erasmus Mundus ENC Network. N.C. was on the Marie Curie ITN grant 'Neuromics'. ACKNOWLEDGEMENTS We would like to thank J.M. Israel and A.J. Timmerman for their assistance with the SON dissections. KEC was sponsored by the Erasmus Mundus ENC Network, the University of Bordeaux and Vrije University. PvN and ABS received support from HEALTH-2009-2.1.2-1 EU-FP7 'SynSys' (#242167). SHRO is supported by Inserm, University of Bordeaux and La Fondation pour la Recherche Médicale (Equipe FRM). ACKNOWLEDGMENTS PR-R was supported by the 'Neuromics' Marie Curie Early stage Training grant (MEST-CT-2005-020919). KEC was supported by the Erasmus Mundus ENC Network, the University of Bordeaux, and Vrije University Amsterdam. NP was supported by the 'BrainTrain' Marie Curie International Training grant (238055). PvN, ABS and SS were supported by SYN-SYS (242167). ACKNOWLEDGEMENTS We would like to thank M. Loos, R. van der Loo, and R. Wijnands for their assistance with the behavioral paradigms and M. Dubey for discussion on Mlc1. KEC was sponsored by the Erasmus Mundus ENC Network, the University of Bordeaux and Vrije University. ABS and SS received support from HEALTH-2009-2.1.2-1 EU-FP7 'SynSys' (#242167). SHRO is supported by Inserm, University of Bordeaux and La Fondation pour la Recherche Médicale (Equipe FRM). ACKNOWLEDGEMENTS At long last this epic chapter of my life has come to an end. It has been filled with trials and tribulations of every sort, and I certainly could not have made it through on my own. Acknowledgements 246 are thanks to you! Sabine, an extra thanks for being a second mom and trying to cheer me up all the times I have been down.

Thank you to the members of my thesis committee who took the time to read my thesis, give thoughtful comments, and make the journey to Amsterdam just before Christmas.

Ronald and Michel, although we did not complete research projects together, our discussions of "How Science Works" have been equally instructive and I am thankful that you have taken the time to share your knowledge and experiences with me.

To the proteomics team, I most certainly could not have done this without you! Pim, I feel compelled to put you as the official second author on my entire thesis. No one has spent more hours sitting side by side in the trenches with me as you have, and I know you did a great deal of work for my project on your own time as well. I hope you can rest easy now knowing that after this I will not send you any more mails asking to "try the analysis another way", but I cannot make any promises about what Mark will do! I am grateful that you patiently explained the same things about R/data analysis/mass spectrometry/statistics over and over again to me with an ample number of sketches. Enjoy all my pens, thief. Remco, Roel, Iryna, and Jochem, thank you for always helping me with my mass spec analyses and keeping an eye of those temperamental machines! Yvonne and Marion, thank you for sharing all your practical lab knowledge and making sure we have all the supplies for our experiments, as long as people remember to write them on the board! Rolinka and Ruud, thank you for teaching me how to do behavioral experiments, looking after my animals, and showing me how to properly work in the mouse house.

Maarten, thank you for all the advice on behavior and statics and help with the MLC1 mice.

CHAPTER 3

Proteomic analysis of gliosomes from mouse brain: identification and investigation of glial membrane proteins ABSTRACT Astrocytes are increasingly recognized as crucial contributors to neuronal function at synapses, axons, and somas. Reliable methods that can provide insight into the astrocyte proteins at the neuron-astrocyte functional interface are highly desirable. Here we conducted a mass spectrometry analysis of Percoll ® gradient isolated gliosomes, a viable preparation of glial subcellular particles often used to study mechanisms of astrocytic transmitter uptake and release and their regulation. Gliosomes were compared with synaptosomes, a preparation containing the neurotransmitter release machinery, and accordingly synaptosomes were enriched for proteins involved in synaptic vesicle mediated transport.

Interestingly, gliosome preparations were found to be enriched for different classes of known astrocyte proteins, such as VAMP3 (involved in astrocyte exocytosis), Ezrin (perisynaptic astrocyte cytoskeletal protein), and Basigin (astrocyte membrane glycoprotein), as well as for G-protein mediated signaling proteins. Mass spectrometry data are available via ProteomeXchange with the identifier PXD001375. Together, these data provide the first detailed description of the gliosome proteome and show that gliosomes can be a useful preparation to study glial membrane proteins and associated processes.

SUPPORTING INFORMATION

Supporting figure 1: Validation of mass spectrometry measurements of homogenate samples. Bar graph depicts log2 enrichment +SEM of indicated proteins as compared to homogenate measured on the same set of samples (n=3-5) by mass spectrometry and immunoblot. Corresponding immunoblots can be found in Figure 2. * FDR p ≤ 10% for mass spectrometry measurements and p ≤ 0.05 for immunoblots as determined by a one-tailed paired Student's t-test.

Functional protein group analysis

All quantified proteins assigned to one of 19 functional synaptic protein groups as defined previously [196,279].

Immunoblot analysis

Protein samples (3-5 µg) were dissolved in SDS loading buffer and used for immunoblotting (Biorad) using antibodies against specific proteins (Table S1). These proteins were chosen based on the availability of antibodies. To correct for input differences, we compared the total protein amount from each sample [181,275,280], as this is a reliable method that is not dependent on a single protein for normalization. Samples were loaded on Criterion TGX Stain-Free 4-12% gels (Biorad), activated and scanned using a Gel Doc Easy Scanner (Biorad) for 5 minutes. Gel volumes obtained from each lane were background-subtracted and used for normalization with the corresponding background-subtracted band intensity values from the immunoblot.

SUPPLEMENTAL INFORMATION

Supplemental Figure S1. Experimental setup for analysis of the hippocampal synaptic membrane proteome after fear conditioning. After the fear conditioning paradigm using a delayed or immediate shock, the dorsal hippocampus is dissected, and synaptic membranes are isolated using sucrose gradients from a pool of 3 mice, with n=6 biological replicates. Subsequent labeling by the eight different iTRAQ labels allows these eight samples to run in a single set, with 3 independent sets being run by mass spectrometry. Finally, the data was analyzed and validated by immunoblotting. Opposite regulation due to learning after a delayed shock and stress of an immediate shock for endocytosis proteins. Correlates of temporal expression profiles for all 25 endocytosis proteins analyzed. From these, 23 were regulated at the 4 h time point (DS4h-IS4h). The correlation graph depicts whether they were also regulated by fear memory (DS4h-1h), after a stressful experience (IS4h-1h), after both, or after none. S1. Proteins used for immunoblot analysis. Shown are proteins with their protein and gene name, the antibody concentration, and the company. S4. Comparing delayed and immediate shock group with a no shock control. For different functional groups, the protein validated (protein and gene name), its regulation (4 h vs. 1 h, or DS/IS vs. NS), SEM, and q-or p-value is indicated for the iTRAQ and immunoblot analysis, respectively. Significance (<0.05) and trend (<0.20) for q-or p-values are highlighted.

Supplemental

Supplemental Table

Supplemental Table

CHAPTER 6

Involvement of astrocyte proteins in contextual fear memory consolidation

Karen E. Carney 1,2,3 , Sabine Spijker 1 , Marjo S. van der Knaap 4,5 , Stéphane H.R. Oliet 2,3 , August B. Smit 1 , Mark H.G. Verheijen 1

ABSTRACT

The process of memory consolidation begins at the moment of acquisition and can extend for years after the initial event has occurred. The consolidation process is traditionally viewed as a reorganization of synaptic components and neuronal rewiring, but recent evidence suggests an additional critical role for astrocytes surrounding the pre-and postsynaptic elements, together forming the tripartite synapse. Here we set out to identify novel astrocyte proteins involved in memory consolidation processes at the synapse. For this, astrocyte-enriched gene lists were used for data mining on published consolidation data sets, and on existing proteomic data sets of mouse hippocampal synaptic membranes four hours after contextual fear conditioning. Also, proteins expressed in rat supraoptic nuclei undergoing hyperosmosis and lactation-induced structural plasticity were included in this search. This resulted in a list of 15 astrocyte proteins potentially regulated at the synapse during memory consolidation.

The expression levels of these proteins were evaluated by immunoblotting on hippocampal synaptosomes isolated from mice during the synaptic phase (4 h) or different times during the systems phase (7, 24, 48, and 72 h) of contextual fear memory consolidation. Distinct temporal patterns of protein regulation emerged: 1) proteins not regulated by this paradigm (Aldoc, Eno1, Glo1, GLT-1), 2) a protein regulated exclusively during the synaptic consolidation window (Ppap2b), 3) proteins regulated during both synaptic and systems phases of consolidation (Ezr, GFAP, Ndrg2, and Atp1a2), and 4) proteins regulated only in the systems phase of consolidation (Bsg, GS, Echs1, GLAST, GAT3, and MLC1). Interestingly, to validate the principal of our analysis, memory function of MLC1 null mice was assed 24 h after consolidation, the time point when Mlc1 expression level changes had been observed. Indeed, Mlc1 null mice showed impaired contextual fear memory, while working memory, as assessed by a T-maze, showed a trend towards impairment. Together these findings demonstrate that fear memory consolidation is accompanied by dynamic changes in a wide range of astrocyte proteins, indicating a role for astrocytes proteins during memory consolidation. "There comes a time when you have to choose between turning the page and closing the book."

Statistics