Molecular analysis of Sigma-1 receptor modulation of the dopamine transporter

Sigma (σ) receptors are well established as a non-opioid, non-phencyclidine, and haloperidol-sensitive receptor family with its own binding profile and a characteristic distribution in the central nervous system (CNS) as well as in endocrine, immune, and some peripheral tissues. Two σ receptors subtypes, termed σ1 and σ2, have been pharmacologically characterized, but, to date, only the σ1 has also been cloned. Activation of σ1 receptors alter several neurotransmitter systems and dopamine (DA) neurotrasmission has been often shown to constitute an important target of σ receptors in different experimental models; however the exact role of σ1 receptor in dopaminergic neurotransmission remains unclear. The DA transporter (DAT) modulates the spatial and temporal aspects of dopaminergic synaptic transmission and interprer the primary mechanism by wich dopaminergic neurons terminate the signal transmission. For this reason present studies have been focused in understanding whether, in cell models, the human subtype of σ1 (hσ1) receptor is able to directly modulate the human DA transporter (hDAT). In the first part of this thesis, HEK-293 and SH-SY5Y cells were permanently transfected with the hσ1 receptor. Subsequently, they were transfected with another plasmid for transiently expressing the hDAT. The hDAT activity was estimated using the described [3H]DA uptake assay and the effects of σ ligands were evaluated by measuring the uptaken [3H]DA after treating the cells with known σ agonists and antagonists. Results illustrated in this thesis demonstrate that activation of overexpressed hσ1 receptors by (+)-pentazocine, the σ1 agonist prototype, determines an increase of 40% of the extracellular [3H]DA uptake, in comparison to non-treated controls and the σ1 antagonists BD-1047 and NE-100 prevent the positive effect of (+)-pentazocine on DA reuptake DA is likely to be considered a neurotoxic molecule. In fact, when levels of intracellular DA abnormally invrease, vescicles can’t sequester the DA which is metabolized by MAO (A and B) and COMT with consequent overproduction of oxygen reactive species and toxic catabolites. Stress induced by these molecules leads cells to death. Thus, for the second part of this thesis, experiments have been performed in order to investigate functional alterations caused by the (+)-pentazocine-mediated increase of DA uptake; particularly it has been investigated if the increase of intracellular [DA] could affect cells viability. Results obtained from this study demonstrate that (+)-pentazocine alone increases DA cell toxicity in a concentration-dependent manner only in cells co-expressing hσ1 and hDAT and σ1 antagonists are able to revert the (+)-pentazocine-induced increase of cell susceptibility to DA toxicity. In the last part of this thesis, the functional cross-talking between hσ1 receptor and hDAT has been further investigated using confocal microscopy. From the acquired data it could be suggested that, following exposure to (+)-pentazocine, the hσ1 receptors massively translocate towards the plasma membrane and colocalize with the hDATs. However, any physical interaction between the two proteins remains to be proved. In conclusion, the presented study shows for the first time that, in cell models, hσ1 receptors directly modulate the hDAT activity. Facilitation of DA uptake induced by (+)-pentazocine is reflected on the increased cell susceptibility to DA toxicity; these effects are prevented by σ1 selective antagonists. Since numerous compounds, including several drugs of abuse, bind to σ1 receptors and activating them could facilitate the damage of dopaminergic neurons, the reported protective effect showed by σ1 antagonists would represent the pharmacological basis to test these compounds in experimental models of dopaminergic neurodegenerative diseases (i.e. Parkinson’s Disease).

Mathematical models of cognitive processes

The research activity carried out during the PhD course was focused on the development of mathematical models of some cognitive processes and their validation by means of data present in literature, with a double aim: i) to achieve a better interpretation and explanation of the great amount of data obtained on these processes from different methodologies (electrophysiological recordings on animals, neuropsychological, psychophysical and neuroimaging studies in humans), ii) to exploit model predictions and results to guide future research and experiments. In particular, the research activity has been focused on two different projects: 1) the first one concerns the development of neural oscillators networks, in order to investigate the mechanisms of synchronization of the neural oscillatory activity during cognitive processes, such as object recognition, memory, language, attention; 2) the second one concerns the mathematical modelling of multisensory integration processes (e.g. visual-acoustic), which occur in several cortical and subcortical regions (in particular in a subcortical structure named Superior Colliculus (SC)), and which are fundamental for orienting motor and attentive responses to external world stimuli. This activity has been realized in collaboration with the Center for Studies and Researches in Cognitive Neuroscience of the University of Bologna (in Cesena) and the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA). PART 1. Objects representation in a number of cognitive functions, like perception and recognition, foresees distribute processes in different cortical areas. One of the main neurophysiological question concerns how the correlation between these disparate areas is realized, in order to succeed in grouping together the characteristics of the same object (binding problem) and in maintaining segregated the properties belonging to different objects simultaneously present (segmentation problem). Different theories have been proposed to address these questions (Barlow, 1972). One of the most influential theory is the so called “assembly coding”, postulated by Singer (2003), according to which 1) an object is well described by a few fundamental properties, processing in different and distributed cortical areas; 2) the recognition of the object would be realized by means of the simultaneously activation of the cortical areas representing its different features; 3) groups of properties belonging to different objects would be kept separated in the time domain. In Chapter 1.1 and in Chapter 1.2 we present two neural network models for object recognition, based on the “assembly coding” hypothesis. These models are networks of Wilson-Cowan oscillators which exploit: i) two high-level “Gestalt Rules” (the similarity and previous knowledge rules), to realize the functional link between elements of different cortical areas representing properties of the same object (binding problem); 2) the synchronization of the neural oscillatory activity in the γ-band (30-100Hz), to segregate in time the representations of different objects simultaneously present (segmentation problem). These models are able to recognize and reconstruct multiple simultaneous external objects, even in difficult case (some wrong or lacking features, shared features, superimposed noise). In Chapter 1.3 the previous models are extended to realize a semantic memory, in which sensory-motor representations of objects are linked with words. To this aim, the network, previously developed, devoted to the representation of objects as a collection of sensory-motor features, is reciprocally linked with a second network devoted to the representation of words (lexical network) Synapses linking the two networks are trained via a time-dependent Hebbian rule, during a training period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from linguistic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with some shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits). PART 2. The ability of the brain to integrate information from different sensory channels is fundamental to perception of the external world (Stein et al, 1993). It is well documented that a number of extraprimary areas have neurons capable of such a task; one of the best known of these is the superior colliculus (SC). This midbrain structure receives auditory, visual and somatosensory inputs from different subcortical and cortical areas, and is involved in the control of orientation to external events (Wallace et al, 1993). SC neurons respond to each of these sensory inputs separately, but is also capable of integrating them (Stein et al, 1993) so that the response to the combined multisensory stimuli is greater than that to the individual component stimuli (enhancement). This enhancement is proportionately greater if the modality-specific paired stimuli are weaker (the principle of inverse effectiveness). Several studies have shown that the capability of SC neurons to engage in multisensory integration requires inputs from cortex; primarily the anterior ectosylvian sulcus (AES), but also the rostral lateral suprasylvian sulcus (rLS). If these cortical inputs are deactivated the response of SC neurons to cross-modal stimulation is no different from that evoked by the most effective of its individual component stimuli (Jiang et al 2001). This phenomenon can be better understood through mathematical models. The use of mathematical models and neural networks can place the mass of data that has been accumulated about this phenomenon and its underlying circuitry into a coherent theoretical structure. In Chapter 2.1 a simple neural network model of this structure is presented; this model is able to reproduce a large number of SC behaviours like multisensory enhancement, multisensory and unisensory depression, inverse effectiveness. In Chapter 2.2 this model was improved by incorporating more neurophysiological knowledge about the neural circuitry underlying SC multisensory integration, in order to suggest possible physiological mechanisms through which it is effected. This endeavour was realized in collaboration with Professor B.E. Stein and Doctor B. Rowland during the 6 months-period spent at the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA), within the Marco Polo Project. The model includes four distinct unisensory areas that are devoted to a topological representation of external stimuli. Two of them represent subregions of the AES (i.e., FAES, an auditory area, and AEV, a visual area) and send descending inputs to the ipsilateral SC; the other two represent subcortical areas (one auditory and one visual) projecting ascending inputs to the same SC. Different competitive mechanisms, realized by means of population of interneurons, are used in the model to reproduce the different behaviour of SC neurons in conditions of cortical activation and deactivation. The model, with a single set of parameters, is able to mimic the behaviour of SC multisensory neurons in response to very different stimulus conditions (multisensory enhancement, inverse effectiveness, within- and cross-modal suppression of spatially disparate stimuli), with cortex functional and cortex deactivated, and with a particular type of membrane receptors (NMDA receptors) active or inhibited. All these results agree with the data reported in Jiang et al. (2001) and in Binns and Salt (1996). The model suggests that non-linearities in neural responses and synaptic (excitatory and inhibitory) connections can explain the fundamental aspects of multisensory integration, and provides a biologically plausible hypothesis about the underlying circuitry.

Neuroprotective strategies against neurodegeneration in cellular model systems

In the present study we analyzed new neuroprotective therapeutical strategies in PD (Parkinson’s disease) and AD (Alzheimer’s disease). Current therapeutic strategies for treating PD and AD offer mainly transient symptomatic relief but it is still impossible to block the loss of neuron and then the progression of PD and AD. There is considerable consensus that the increased production and/or aggregation of α- synuclein (α-syn) and β-amyloid peptide (Aβ), plays a central role in the pathogenesis of PD, related synucleinopathies and AD. Therefore, we identified antiamyloidogenic compounds and we tested their effect as neuroprotective drug-like molecules against α-syn and β-amyloid cytotoxicity in PC12. Herein, we show that two nitro-catechol compounds (entacapone and tolcapone) and 5 cathecol-containing compounds (dopamine, pyrogallol, gallic acid, caffeic acid and quercetin) with antioxidant and anti-inflammatory properties, are potent inhibitors of α-syn and β-amyloid oligomerization and fibrillization. Subsequently, we show that the inhibition of α-syn and β-amyloid oligomerization and fibrillization is correlated with the neuroprotection of these compounds against the α-syn and β-amyloid-induced cytotoxicity in PC12. Finally, we focused on the study of the neuroprotective role of microglia and on the possibility that the neuroprotection properties of these cells could be use as therapeutical strategy in PD and AD. Here, we have used an in vitro model to demonstrate neuroprotection of a 48 h-microglial conditioned medium (MCM) towards cerebellar granule neurons (CGNs) challenged with the neurotoxin 6-hydroxydopamine (6-OHDA), which induces a Parkinson-like neurodegeneration, with Aβ42, which induces a Alzheimer-like neurodegeneration, and glutamate, involved in the major neurodegenerative diseases. We show that MCM nearly completely protects CGNs from 6-OHDA neurotoxicity, partially from glutamate excitotoxicity but not from Aβ42 toxin.

Caratterizzazione morfologica e neurochimica dei neuroni sensitivi e motori del trigono vescicale e del muscolo uretrale di maiale

Il trigono della vescica urinaria (UBT) è un'area limitata attraverso la quale penetrano nella vescica la maggior parte dei vasi e fibre e in cui le fibre nervose e neuroni intramurali sono più concentrati. Mediante l’utilizzo combinato di un tracciante retrogrado(FB) e dell’immunoistochimica sono stati valutati il fenotipo e l’area del soma dei neuroni dei gangli spinali (DRG), dei neuroni post-gangliari, il fenotipo dei gangli della catena simpatica (STG) e i gangli mesenterici caudali (CMG) innervanti l’UBT. - Caratterizzazione dei neuroni dei DRG con: peptide correlato al gene della calcitonina (CGRP)(30±3%, 29±3%, rispettivamente), sostanza P(SP)(26±8%, 27±12%), ossido nitrico sintasi neuronale (nNOS)(21±4%; 26±7%), neurofilamento 200kDa (NF200)(75±14%, 81±7% ) , transient receptor potential vanilloid1 (TRPV1)(48±13%, 43±6%) e isolectina-B4-positivi (IB4) (56±6%;43±10%). I neuroni sensoriali, distribuiti da L2 a Ca1 (DRG), hanno presentato una localizzazione segmentale, mostrando maggior densità nei DRG L4-L5 e S2-S4. I neuroni sensoriali lombari sono risultati significativamente più grandi di quelle sacrali (1.112±624μm2 vs716±421μm2). Complessivamente, questi dati indicano che le vie lombari e sacrali probabilmente svolgono ruoli diversi nella trasmissione sensitiva del trigono della vescica urinaria. -I neuroni FB+ della STG e dei CMG sono risultati immunoreattivi per la tirosina idrossilasi (TH)(66±10,1%, 53±8,2%, rispettivamente), la dopamina beta-idrossilasi (DβH)(62±6,2%, 52±6,2%), neuropeptideY (NPY)(59±8%; 66±7%), CGRP(24±3%, 22±3%), SP(22±2%; 38±8%), polipeptide intestinale vasoattivo (VIP)(19±2%; 35±4%), nNOS(15±2%; 33±8%), trasportatore vescicolare dell'acetilcolina (VAChT)(15±2%; 35±5%), leu-encefalina (LENK)(14±7%; 26±9%), e somatostatina (SOM)(12±3%;32±7%).Il numero medio di neuroni FB+ (1845,1±259,3) era nella STG in L1-S3, con i pirenofori più piccoli (465,6±82.7μm2). Un gran numero (4287,5±1450,6) di neuroni FB+ di piccole dimensioni (476,1±103,9μm2) sono stati localizzati lungo il margine dei CMG. Il maggior numero (4793,3±1990,8) di neuroni FB + è stato osservato nel plesso pelvico, dove i neuroni marcati erano raggruppati in micro-gangli e con pirenoforo ancora più piccolo (374,9±85,4 μm2).

Motor control system in Parkinson’s disease: a modeling approach

Parkinson’s disease is a neurodegenerative disorder due to the death of the dopaminergic neurons of the substantia nigra of the basal ganglia. The process that leads to these neural alterations is still unknown. Parkinson’s disease affects most of all the motor sphere, with a wide array of impairment such as bradykinesia, akinesia, tremor, postural instability and singular phenomena such as freezing of gait. Moreover, in the last few years the fact that the degeneration in the basal ganglia circuitry induces not only motor but also cognitive alterations, not necessarily implicating dementia, and that dopamine loss induces also further implications due to dopamine-driven synaptic plasticity got more attention. At the present moment, no neuroprotective treatment is available, and even if dopamine-replacement therapies as well as electrical deep brain stimulation are able to improve the life conditions of the patients, they often present side effects on the long term, and cannot recover the neural loss, which instead continues to advance. In the present thesis both motor and cognitive aspects of Parkinson’s disease and basal ganglia circuitry were investigated, at first focusing on Parkinson’s disease sensory and balance issues by means of a new instrumented method based on inertial sensor to provide further information about postural control and postural strategies used to attain balance, then applying this newly developed approach to assess balance control in mild and severe patients, both ON and OFF levodopa replacement. Given the inability of levodopa to recover balance issues and the new physiological findings than underline the importance in Parkinson’s disease of non-dopaminergic neurotransmitters, it was therefore developed an original computational model focusing on acetylcholine, the most promising neurotransmitter according to physiology, and its role in synaptic plasticity. The rationale of this thesis is that a multidisciplinary approach could gain insight into Parkinson’s disease features still unresolved.