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Die Erkrankung Huntington s Chorea ist eine autosomal dominant vererbte Erkrankung, die gewöhnlich im mittleren Lebensabschnitt beginnt und unausweichlich zum Tode führt. In unserem Bestreben, Proteine zu identifizieren, welche an Prozessen "Upstream" oder "Downstream" des krankheitsverursachenden Proteins Huntingtin beteiligt sind, wurde das Proteom eines sehr gut etablierten Mausmodells mit Hilfe der Großgel 2D-Elektrophorese untersucht. Es konnte zum ersten Mal auf Proteinebene nachweisen werden, dass die Expression von zwei Serinproteasehemmern, alpha1-Antitrypsin und Contraspin und darüber hinaus eines Chaperons, alphaB-Kristallin, im Verlauf der Erkrankung abnimmt. Reduzierte Expression von alpha1-Antitrypsin und Contraspin konnte in Gehirn, Leber, Herz und Testes nahe dem Endstadium der Erkrankung nachgewiesen werden. Hier ist es wichtig festzustellen, dass die Expressionsabnahme von alpha1-Antitrypsin im Gehirn der Abnahme in der Leber im Herzen und in den Testes vorangeht. Eine verminderte Expression des Chaperons alphaB-Kristallin wurde nur im Gehirn gefunden. Für ein weiteres Protein, das Major Urinary Protein, wurde eine verminderte Expression in der Leber und im Urin von betroffenen Mäusen festgestellt. Damit konnte demonstriert werden, dass die Erkrankung auf Proteinebene auch ein Protein, das im Gehirn von transgenen Mäusen nicht vorkommt, beeinflusst. Bei Untersuchungen am Menschen wurde in drei Gehirnregionen von Postmortem-Gehirnen von Huntington s Chorea Patienten eine veränderte Expression von alpha1-Antitrypsin festgestellt. Wenn gewährleistet werden kann, dass die Konzentration von alpha1-Antitrypsin und alphaB-Kristallin während Huntington s Chorea im Gewebe nicht absinkt, könnte dies vielleicht neuronalen Zelltod verhindern und somit bei der Verzögerung des Krankheitsverlaufs nutzbringend eingesetzt werden. / Huntington disease is an autosomal dominantly inherited disease that usually starts in midlife and inevitably leads to death. In an effort to identify proteins involved in processes upstream or downstream of the disease causing huntingtin, the proteome of a well-established mouse model was studied by large-gel 2D electrophoresis. It could be demonstrated for the first time at the protein level that two serin protease inhibitors, alpha1-antitrypsin and contraspin and the chaperone alphaB-crystallin decrease in expression over the course of disease. Importantly, the alpha1-antitrypsin decrease in the brain precedes that in liver, heart and testes in mice. Reduced expression of alpha1-antitrypsin and contraspin could be detected in the brain, liver heart and testes close to terminal disease. Decreased expression of the chaperone alphaB-crystallin was found exclusively in the brain. Reduced expression of the liver specific major urinary proteins not found in the brain, was seen in affected mice, demonstrating that the disease exerts its influence on a protein not present in the brain of transgenic mice at the protein level. When investigating three human brain regions obtained post-mortem from Huntington s disease patients, alpha1-antitrypsin expression was also altered. Maintaining alpha1-antitrypsin and alphaB-crystallin availability during the course of Huntington s disease might prevent neuronal cell death and therefore could be useful in delaying the disease progression.

Despite significant progress in the development of anti-cancer drugs, there is still a need for novel therapeutic strategies that would improve the outcome of cancer patients. Using proteomic technologies and cell lines with different phenotype of p53 tumour suppressor, we monitored cancer cell response to anti-cancer treatment with focus on the development of drug resistance. The different levels of metabolic proteins were identified in our study which may help to explain different anti-cancer activity of drugs with only a subtle difference in structure. More importantly, proteins associated with the development of drug resistance were identified and such expression changes have become a focus of interest. Our findings demonstrate a higher protein level of serine hydroxymethyltransferase, serpin B5 and calretinin in cancer cells resistant to Aurora kinase inhibitors. Such proteins promote the tumour growth with no apparent impact of p53 phenotype whilst voltage-dependent anion-selective channel protein 2 contributes to the development of resistance only in cells with functional p53 which is accompanied by the decreased level of elongation factor 2. On the other hand, cancer cells with loss of p53 appear to amplify alternative mechanisms such as protection against oxidative stress. The results...

Casamento de padrões de pontos é um problema fundamental em reconhecimento de padrões. O objetivo é encontrar uma correspondência entre dois conjuntos de pontos, associados a características relevantes de objetos ou entidades, mapeando os pontos de um conjunto no outro. Este problema está associado a muitas aplicações, como por exemplo, reconhecimento de objetos baseado em modelos, imagens estéreo, registro de imagens, biometria, entre outros. Para encontrar um mapeamento, os objetos são codificados por representações abstratas, codificando as características relevantes consideradas na comparação entre pares de objetos. Neste trabalho, objetos são representados por grafos, codificando tanto as características `locais\' quanto as relações espaciais entre estas características. A comparação entre objetos é guiada por uma formulação de atribuição quadrática, que é um problema NP-difícil. Para estimar uma solução, duas técnicas de casamento entre grafos são propostas: uma baseada em grafos auxiliares, chamados de grafos deformados; e outra baseada em representações `esparsas\', campos aleatórios de Markov e propagação de crenças. Devido as suas respectivas limitações, as abordagens são adequadas para situações específicas, conforme mostrado neste documento. Resultados envolvendo as duas abordagens são ilustrados em quatro importantes aplicações: casamento de imagens de gel eletroforese 2D, segmentação interativa de imagens naturais, casamento de formas, e colorização assistida por computador. / Point set matching is a fundamental problem in pattern recognition. The goal is to match two sets of points, associated to relevant features of objects or entities, by finding a mapping, or a correspondence, from one set to another set of points. This issue arises in many applications, e.g. model-based object recognition, stereo matching, image registration, biometrics, among others. In order to find a mapping, the objects can be encoded by abstract representations, carrying relevant features which are taken into account to compare pairs of objects. In this work, graphs are adopted to represent the objects, encoding their `local\' features and the spatial relations between these features. The comparison of two given objects is guided by a quadratic assignment formulation, which is NP-hard. In order to estimate the optimal solution, two approximations techniques, via graph matching, are proposed: one is based on auxiliary graphs, called deformed graphs; the other is based on `sparse\' representations, Markov random fields and belief propagation. Due to their respective limitations, each approach is more suitable to each specific situation, as shown in this document. The quality of the two approaches is illustrated on four important applications: 2D electrophoresis gel matching, interactive natural image segmentation, shape matching, and computer-assisted colorization.

Casamento de padrões de pontos é um problema fundamental em reconhecimento de padrões. O objetivo é encontrar uma correspondência entre dois conjuntos de pontos, associados a características relevantes de objetos ou entidades, mapeando os pontos de um conjunto no outro. Este problema está associado a muitas aplicações, como por exemplo, reconhecimento de objetos baseado em modelos, imagens estéreo, registro de imagens, biometria, entre outros. Para encontrar um mapeamento, os objetos são codificados por representações abstratas, codificando as características relevantes consideradas na comparação entre pares de objetos. Neste trabalho, objetos são representados por grafos, codificando tanto as características `locais\' quanto as relações espaciais entre estas características. A comparação entre objetos é guiada por uma formulação de atribuição quadrática, que é um problema NP-difícil. Para estimar uma solução, duas técnicas de casamento entre grafos são propostas: uma baseada em grafos auxiliares, chamados de grafos deformados; e outra baseada em representações `esparsas\', campos aleatórios de Markov e propagação de crenças. Devido as suas respectivas limitações, as abordagens são adequadas para situações específicas, conforme mostrado neste documento. Resultados envolvendo as duas abordagens são ilustrados em quatro importantes aplicações: casamento de imagens de gel eletroforese 2D, segmentação interativa de imagens naturais, casamento de formas, e colorização assistida por computador. / Point set matching is a fundamental problem in pattern recognition. The goal is to match two sets of points, associated to relevant features of objects or entities, by finding a mapping, or a correspondence, from one set to another set of points. This issue arises in many applications, e.g. model-based object recognition, stereo matching, image registration, biometrics, among others. In order to find a mapping, the objects can be encoded by abstract representations, carrying relevant features which are taken into account to compare pairs of objects. In this work, graphs are adopted to represent the objects, encoding their `local\' features and the spatial relations between these features. The comparison of two given objects is guided by a quadratic assignment formulation, which is NP-hard. In order to estimate the optimal solution, two approximations techniques, via graph matching, are proposed: one is based on auxiliary graphs, called deformed graphs; the other is based on `sparse\' representations, Markov random fields and belief propagation. Due to their respective limitations, each approach is more suitable to each specific situation, as shown in this document. The quality of the two approaches is illustrated on four important applications: 2D electrophoresis gel matching, interactive natural image segmentation, shape matching, and computer-assisted colorization.