Diss. (sammanfattning) Stockholm : Karol. inst., 2004. / Härtill 4 uppsatser.

Dissertation Thesis (Ph.D.)--University of South Florida, 2007. / Includes vita. Includes bibliographical references (leaves 178-202). Also available online.

Thesis (MTchLn)--University of Canterbury, 2008. / Typescript (photocopy). Includes bibliographical references (leaves 81-98). Also available via the World Wide Web.

Thesis (Ph.D. in Neuroscience) -- University of Colorado, 2005. / Typescript. Includes bibliographical references (leaves 96-111). Free to UCDHSC affiliates. Online version available via ProQuest Digital Dissertations;

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (leaves 48-60).

Neurons, the building blocks of the brain, are an astonishingly capable type of cell. Collectively they can store, manipulate and retrieve biologically important information, allowing animals to learn and adapt to environmental changes. This universal adaptability is widely believed to be due to plasticity: the readiness of neurons to manipulate and adjust their intrinsic properties and strengths of connections to other cells. It is through such modifications that associations between neurons can be made, giving rise to memory representations; for example, linking a neuron responding to the smell of pancakes with neurons encoding sweet taste and general gustatory pleasure. However, this malleability inherent to neuronal cells poses a dilemma from the point of view of stability: how is the brain able to maintain stable operation while in the state of constant flux? First of all, won’t there occur purely technical problems akin to short-circuiting or runaway activity? And second of all, if the neurons are so easily plastic and changeable, how can they provide a reliable description of the environment? Of course, evidence abounds to testify to the robustness of brains, both from everyday experience and scientific experiments. How does this robustness come about? Firstly, many control feedback mechanisms are in place to ensure that neurons do not enter wild regimes of behaviour. These mechanisms are collectively known as homeostatic plasticity, since they ensure functional homeostasis through plastic changes. One well-known example is synaptic scaling, a type of plasticity ensuring that a single neuron does not get overexcited by its inputs: whenever learning occurs and connections between cells get strengthened, subsequently all the neurons’ inputs get downscaled to maintain a stable level of net incoming signals. And secondly, as hinted by other researchers and directly explored in this work, networks of neurons exhibit a property present in many complex systems called sloppiness. That is, they produce very similar behaviour under a wide range of parameters. This principle appears to operate on many scales and is highly useful (perhaps even unavoidable), as it permits for variation between individuals and for robustness to mutations and developmental perturbations: since there are many combinations of parameters resulting in similar operational behaviour, a disturbance of a single, or even several, parameters does not need to lead to dysfunction. It is also that same property that permits networks of neurons to flexibly reorganize and learn without becoming unstable. As an illustrative example, consider encountering maple syrup for the first time and associating it with pancakes; thanks to sloppiness, this new link can be added without causing the network to fire excessively. As has been found in previous experimental studies, consistent multi-neuron activity patterns arise across organisms, despite the interindividual differences in firing profiles of single cells and precise values of connection strengths. Such activity patterns, as has been furthermore shown, can be maintained despite pharmacological perturbation, as neurons compensate for the perturbed parameters by adjusting others; however, not all pharmacological perturbations can be thus amended. In the present work, it is for the first time directly demonstrated that groups of neurons are by rule sloppy; their collective parameter space is mapped to reveal which are the sensitive and insensitive parameter combinations; and it is shown that the majority of spontaneous fluctuations over time primarily affect the insensitive parameters. In order to demonstrate the above, hippocampal neurons of the rat were grown in culture over multi-electrode arrays and recorded from for several days. Subsequently, statistical models were fit to the activity patterns of groups of neurons to obtain a mathematically tractable description of their collective behaviour at each time point. These models provide robust fits to the data and allow for a principled sensitivity analysis with the use of information-theoretic tools. This analysis has revealed that groups of neurons tend to be governed by a few leader units. Furthermore, it appears that it was the stability of these key neurons and their connections that ensured the stability of collective firing patterns across time. The remaining units, in turn, were free to undergo plastic changes without risking destabilizing the collective behaviour. Together with what has been observed by other researchers, the findings of the present work suggest that the impressively adaptable yet robust functioning of the brain is made possible by the interplay of feedback control of few crucial properties of neurons and the general sloppy design of networks. It has, in fact, been hypothesised that any complex system subject to evolution is bound to rely on such design: in order to cope with natural selection under changing environmental circumstances, it would be difficult for a system to rely on tightly controlled parameters. It might be, therefore, that all life is just, by nature, sloppy.

abstract: Dendrites are the structures of a neuron specialized to receive input signals and to provide the substrate for the formation of synaptic contacts with other cells. The goal of this work is to study the activity-dependent mechanisms underlying dendritic growth in a single-cell model. For this, the individually identifiable adult motoneuron, MN5, in Drosophila melanogaster was used. This dissertation presents the following results. First, the natural variability of morphological parameters of the MN5 dendritic tree in control flies is not larger than 15%, making MN5 a suitable model for quantitative morphological analysis. Second, three-dimensional topological analyses reveals that different parts of the MN5 dendritic tree innervate spatially separated areas (termed "isoneuronal tiling"). Third, genetic manipulation of the MN5 excitability reveals that both increased and decreased activity lead to dendritic overgrowth; whereas decreased excitability promoted branch elongation, increased excitability enhanced dendritic branching. Next, testing the activity-regulated transcription factor AP-1 for its role in MN5 dendritic development reveals that neural activity enhanced AP-1 transcriptional activity, and that AP-1 expression lead to opposite dendrite fates depending on its expression timing during development. Whereas overexpression of AP-1 at early stages results in loss of dendrites, AP-1 overexpression after the expression of acetylcholine receptors and the formation of all primary dendrites in MN5 causes overgrowth. Fourth, MN5 has been used to examine dendritic development resulting from the expression of the human gene MeCP2, a transcriptional regulator involved in the neurodevelopmental disease Rett syndrome. Targeted expression of full-length human MeCP2 in MN5 causes impaired dendritic growth, showing for the first time the cellular consequences of MeCP2 expression in Drosophila neurons. This dendritic phenotype requires the methyl-binding domain of MeCP2 and the chromatin remodeling protein Osa. In summary, this work has fully established MN5 as a single-neuron model to study mechanisms underlying dendrite development, maintenance and degeneration, and to test the behavioral consequences resulting from dendritic growth misregulation. Furthermore, this thesis provides quantitative description of isoneuronal tiling of a central neuron, offers novel insight into activity- and AP-1 dependent developmental plasticity, and finally, it establishes Drosophila MN5 as a model to study some specific aspects of human diseases. / Dissertation/Thesis / Ph.D. Neuroscience 2012

Resumo não disponível

A estrutura plástica do cérebro tem a capacidade de se adaptar a diversas condições e estímulos. No entanto, isso também pode facilitar a emergência de instabilidades, o que acarreta na necessidade de mecanismos de homeostase que previnam que a dinâmica da rede neural chegue a estados patológicos. A plasticidade associativa é considerada a principal base para o desenvolvimento de funções como memória e aprendizado, a realimentação positiva potencialmente leva à saturação de sinapses e instabilidades de atividade, especialmente em arquiteturas om conectividades recorrentes tais como em microcircuitos cerebrais. Neste trabalho investigamos a difícil interação entre a codificação de informação e o controle da atividade através da plasticidade Hebbiana e do escalonamento sináptico homeostático. O objetivo é a determinação de propriedades, como por exemplo a inibição e a conectividade, que proporcionam o desenvolvimento de codificação de informação de uma maneira confiável e fisiologicamente relevante através de plasticidade sináptica, prevenindo comportamento patológico. Após uma breve revisão bibliográfica de tópicos básicos da neurofisiologia e da modelagem de redes neurais, a primeira parte dos resultados apresenta uma rede que, sob uma forma específica de esc alonamento sináptico, desenvolve associatividade de padrões de disparo espaço-temporais e discute a afetação da capacidade de separação e confiabilidade de acordo om escalas de tempo de plasticidade, limitações sobre a eficácia sináptica e a dinâmica das interações inibitórias. A segunda parte define condições para manter o escalonamento sináptico homeostático sem instabilidades dinâmicas, om foco em fenômenos pouco explorados, como o escalonamento de sinapses inibitórias e o alcance efetivo da plasticidade. Em direção a outros mecanismos que podem influenciar esse balanço, a última parte descreve os efeitos do local de expressão da plasticidade de longa duração sobre a dinâmica de aprendizado, o que é demonstrado diferir de acordo om a codificação do estímulo.

Synapses are an early pathological target in a wide range of neurodegenerative conditions including adult-onset Alzheimer’s and Parkinson’s, and diseases of childhood such as spinal muscular atrophy and neuronal ceroid lipofuscinoses (NCLs). However, our understanding of the mechanisms regulating the stability of synapses and their exceptional vulnerability to neurodegenerative stimuli remains in its infancy. To address this, we have used the NCLs to model the molecular alterations underpinning synaptic vulnerability. Our primary objective is to identify novel regulators of synaptic stability as well as highlight novel therapeutic targets which may prove effective across multiple neurodegenerative conditions where synapses are an early pathological target. The NCLs, are the most frequent autosomal-recessive disease of childhood. There are currently 14 individual genes whose mutations result in similar phenotypes including blindness, cognitive/motor deficits, seizures and premature death. This suggests that despite the difference in the initiating mutation and the degenerative processes across this collective group are likely to impact on overlapping pathways. Focusing on two murine models of NCL; one with an infantile onset - CLN1 disease (Ppt1-/-) and one with a juvenile onset - CLN3 disease (Cln3-/-) we made use of the temporo-spatial synaptic vulnerability pattern in these mice to plan proteomic and in silico analyses. This pipeline was utilised to identify perturbed protein candidates and pathways correlating with differential regional synaptic vulnerability. This ultimately allowed the generation of a list of candidate proteins, some of which were relevant to human NCL as they were altered in post mortem brain samples. Interestingly, many of the correlative candidates also appear to show conserved alterations in both NCL forms examined and other neurodegenerative diseases. Next, candidates were genetically and/or pharmacologically targeted to study their modulatory effects on neuronal stability in vivo. This was done using CLN3 Drosophila as a rapid screening assay and led to the successful characterisation of a subset of candidates as either enhancers or suppressors of the CLN3-induced phenotype in vivo. As well as identifying regulators of neuronal stability, following a similar pipeline, we identified a set of putative biomarkers of disease progression in muscle and blood in the Ppt1- /- mice, a subset of which appeared conserved in Cln3-/- mice. One of these conserved candidates presented the same directionality of change in human post mortem brain samples, indicating its relevance to the human NCL. Following this workflow from spatio-temporal profiling of murine synaptic populations, to in silico analyses and in vivo phenotypic assessment, we demonstrate that we can identify multiple protein candidates capable of modulating neuronal stability in vivo and identified putative biomarkers that tracked disease progression.