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In a healthy adult brain, glial cell line-derived neurotrophic factor (GDNF) is exclusively expressed by neurons, and, in some instances, it has also been shown to derive from a single neuronal subpopulation. Secreted GDNF acts in a paracrine fashion by forming a complex with the GDNF family receptor α1 (GFRα1), which is mainly expressed by neurons and can act in cis as a membrane-bound factor or in trans as a soluble factor. The GDNF/GFRα1 complex signals through interactions with the "rearranged during transfection" (RET) receptor or via the neural cell adhesion molecule (NCAM) with a lower affinity. GDNF can also signal independently from GFRα1 by interacting with syndecan-3. RET, which is expressed by neurons involved in several pathways (nigro-striatal dopaminergic neurons, motor neurons, enteric neurons, sensory neurons, etc.), could be the main determinant of the specificity of GDNF's pro-survival effect. In an injured brain, de novo expression of GDNF occurs in glial cells. Neuroinflammation has been reported to induce GDNF expression in activated astrocytes and microglia, infiltrating macrophages, nestin-positive reactive astrocytes, and neuron/glia (NG2) positive microglia-like cells. This disease-related GDNF overexpression can be either beneficial or detrimental depending on the localization in the brain and the level and duration of glial cell activation. Some reports also describe the upregulation of RET and GFRα1 in glial cells, suggesting that GDNF could modulate neuroinflammation.

Parkinson's disease (PD) is a progressive CNS disorder that is primarily associated with impaired movement. PD develops over decades and is linked to the gradual loss of dopamine delivery to the striatum, via the loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc). While the administration of L-dopa and deep brain stimulation are potent therapies, their costs, side effects and gradual loss of efficacy underlines the need to develop other approaches. Unfortunately, the lack of pertinent animal models that reproduce DA neuron loss and behavior deficits-in a timeline that mimics PD progression-has hindered the identification of alternative therapies. A complementary approach to transgenic animals is the use of nonhuman primates (NHPs) combined with the overexpression of disease-related genes using viral vectors. This approach may induce phenotypes that are not influenced by developmental compensation mechanisms, and that take into account the personality of animals. In this review article, we discuss the combination of gene transfer and NHPs to develop "genetic" models of PD that are suitable for testing therapeutic approaches.

Parkinson's disease (PD) is a progressive CNS disorder that is primarily associated with impaired movement. PD develops over decades and is linked to the gradual loss of dopamine delivery to the striatum, via the loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc). While the administration of L-dopa and deep brain stimulation are potent therapies, their costs, side effects and gradual loss of efficacy underlines the need to develop other approaches. Unfortunately, the lack of pertinent animal models that reproduce DA neuron loss and behavior deficits-in a timeline that mimics PD progression-has hindered the identification of alternative therapies. A complementary approach to transgenic animals is the use of nonhuman primates (NHPs) combined with the overexpression of disease-related genes using viral vectors. This approach may induce phenotypes that are not influenced by developmental compensation mechanisms, and that take into account the personality of animals. In this review article, we discuss the combination of gene transfer and NHPs to develop "genetic" models of PD that are suitable for testing therapeutic approaches.

Glial cell line-derived neurotrophic factor (GDNF) and Neurturin (NRTN) bind to a receptor complex consisting of a member of the GDNF family receptor (GFR)-α and the Ret tyrosine kinase. Both factors were shown to protect nigro-striatal dopaminergic neurons and reduce motor symptoms when applied terminally in toxin-induced Parkinson's disease (PD) models. However, clinical trials based on intraputaminal GDNF protein administration or recombinant adeno-associated virus (rAAV)-mediated NRTN gene delivery have been disappointing. In this review, several factors that could have limited the clinical benefits are discussed. Retrograde transport of GDNF/NRTN to the dopaminergic neurons soma is thought to be necessary for NRTN/GFR-α/Ret signaling mediating the pro-survival effect. Therefore, the feasibility of treating advanced patients with neurotrophic factors is questioned by recent data showing that: (i) tyrosine hydroxylase-positive putaminal innervation has almost completely disappeared at 5 years post-diagnosis and (ii) in patients enrolled in the rAAV-NRTN trial more than 5 years post-diagnosis, NRTN was almost not transported to the substantia nigra pars compacta. In addition to its anti-apoptotic and neurotrophic properties, GDNF also interferes with dopamine homeostasis via time and dose-dependent effects such as: stimulation of dopamine neuron excitability, inhibition of dopamine transporter activity, tyrosine hydroxylase phosphorylation, and inhibition of tyrosine hydroxylase transcription. Depending on the delivery parameters, the net result of this intricate network of regulations could be either beneficial or deleterious. In conclusion, further unraveling of the mechanism of action of GDNF gene delivery in relevant animal models is still needed to optimize the clinical benefits of this new therapeutic approach. Recent developments in the design of regulated viral vectors will allow to finely adjust the GDNF dose and period of administration. Finally, new clinical studies in less advanced patients are warranted to evaluate the potential of AAV-mediated neurotrophic factors gene delivery in PD. These will be facilitated by the demonstration of the safety of rAAV administration into the human brain.

The tetracycline-controlled Tet-Off and Tet-On gene expression systems are used to regulate the activity of genes in eukaryotic cells in diverse settings, varying from basic biological research to biotechnology and gene therapy applications. These systems are based on regulatory elements that control the activity of the tetracycline-resistance operon in bacteria. The Tet-Off system allows silencing of gene expression by administration of tetracycline (Tc) or tetracycline-derivatives like doxycycline (dox), whereas the Tet-On system allows activation of gene expression by dox. Since the initial design and construction of the original Tet-system, these bacterium-derived systems have been significantly improved for their function in eukaryotic cells. We here review how a dox-controlled HIV-1 variant was designed and used to greatly improve the activity and dox-sensitivity of the rtTA transcriptional activator component of the Tet-On system. These optimized rtTA variants require less dox for activation, which will reduce side effects and allow gene control in tissues where a relatively low dox level can be reached, such as the brain.

A graph-based model for describing Schemes and instances of object databases together with a graphical data manipulation language based on pattern matching are introduced. The data model allows the explicit modeling of classes and relations which contain objects and associations, respectively. GOAL consists mainly of two operations, the addition and the deletion. These perform on every part. of the instance where a certain pattern is found. We will present. the syntax and the semantics of the language, and show its computational Completeness.

A very short overview is given of the principles of databases. The entity relationship model is used to define the conceptual base. Furthermore file management, the hierarchical model, the network model, the relational model and the object oriented model are discussed During the second world war, computers were used for encoding and decoding messages of the English and German army. Only after the war the computers were used for more reasonable and economical reasons. In the beginning of the fifties problems with typically a small amount of input data but for which complex calculations have to be made, were solved using computers. This was due to the small amount of memory and the long access time that was needed. Typical programming languages for this kind of problems emerged: Fortran, Algol and later on Pascal, C and C++. These languages however, were not user friendly enough, nor were they designed to handle huge amounts of complex data. So in the fifties we switched from simple file management to the use of a data structure that was more complex, but that enabled us to solve problems and answer questions that needed a huge quantity of data. We will first introduce the concept of a database and then we will look to a number of different kinds of databases that have been used from 1960 until now.