Protein-protein interactions (PPIs) are fundamental to a wide array of biological processes, including but not limited to cellular signalling pathways, structural organization within cells, and the mechanisms of various diseases. Decoding these complex systems could pave the way for targeted therapeutic intervention but remains a technical challenge. This dissertation focused on the synergistic integration of high-throughput peptide synthesis with state-of-the- art biophysical methodologies, such as (Microscale Thermophoresis) MST, (Fluorescence Proximity Sensing) FPS, and (Temperature Related Intensity Change) TRIC. Thereby providing a robust platform for both understanding endogenous interactions at a molecular level, and for the rational design of novel effectors and probes. The core of this dissertation is dedicated to the exploration of PPIs that involve intrinsically disordered regions (IDRs), which make up a considerable part of the mammalian proteome. These IDRs often harbour short linear motifs (SLiMs) that are key players in PPIs. Using the µSPOT synthesis, peptides representing these SLiMs are generated in a cost-effective and parallelized manner. These peptides are then used with a variety of biophysical techniques, allowing for both identification of new interactors and thermodynamic and kinetic profiling. Unified by the overarching theme of integrating peptide libraries with quantitative biophysical readouts, I developed both inhibitors and synthetic affinity probes for a range of biological applications. These works collectively leverage the effective display of peptide ligands with detailed proteomic or quantitative read-outs. This was achieved by combining the versatile µSPOT synthesis techniques with mass-spectrometric (MS) and quantitative readouts, namely TRIC, FPS, and MST, thereby enabling the identification of interactors and the precise quantification of protein affinities and binding kinetics at the nanomolar scale. The value of the resulting platform was substantiated by the successful development of small molecule probes as well as multivalent, peptide-based effectors, and fluorescent probes for the specific targeting of selected proteins in various biological contexts. In summary, this dissertation established a versatile and robust biophysical platform for the high-throughput deciphering of PPIs and the development of fluorescent probes and inhibitors for the specific visualisation and modulation of selected target proteins. / Protein-Protein-Interaktionen (PPIs) sind grundlegend für eine Vielzahl biologischer Prozesse, einschließlich, aber nicht beschränkt auf zelluläre Signalwege, strukturelle Organisation innerhalb von Zellen und die Mechanismen verschiedener Krankheiten. Das Entschlüsseln dieser komplexen Systeme könnte den Weg für gezielte therapeutische Interventionen ebnen, stellt jedoch eine technische Herausforderung dar. Diese Dissertation konzentrierte sich auf die synergetische Integration von hochdurchsatz-Peptidsynthese mit modernsten biophysikalischen Methoden wie Mikroskalen-Thermophorese (MST), Fluoreszenz- Näherungssensorik (FPS) und temperaturabhängiger Intensitätsänderung (TRIC). Dadurch wurde eine robuste Plattform sowohl für das Verständnis endogener Interaktionen auf molekularer Ebene als auch für das rationale Design neuer Effektoren und Sonden bereitgestellt. Der Kern dieser Dissertation widmet sich der Erforschung von PPIs, die intrinsisch ungeordnete Regionen (IDRs) involvieren, die einen erheblichen Teil des Säugetierproteoms ausmachen. Diese IDRs beherbergen oft kurze lineare Motive (SLiMs), die eine Schlüsselrolle in PPIs spielen. Mit der µSPOT-Synthese werden Peptide, die diese SLiMs repräsentieren, auf kosteneffiziente und parallele Weise generiert. Diese Peptide werden dann mit verschiedenen biophysikalischen Techniken verwendet, was sowohl die Identifizierung neuer Interaktoren als auch die thermodynamische und kinetische Profilierung ermöglicht. Vereint durch das übergreifende Thema der Integration von Peptidbibliotheken mit quantitativen biophysikalischen Ausleseverfahren habe ich sowohl Inhibitoren als auch synthetische Affinitätssonden für eine Reihe biologischer Anwendungen entwickelt. Diese Arbeiten nutzen kollektiv die effektive Darstellung von Peptidliganden mit detaillierten proteomischen oder quantitativen Ausleseverfahren. Dies wurde erreicht, indem die vielseitigen µSPOT-Synthesetechniken mit massenspektrometrischen (MS) und quantitativen Ausleseverfahren, insbesondere TRIC, FPS und MST, kombiniert wurden, wodurch die Identifizierung von Interaktoren und die präzise Quantifizierung von Protein-Affinitäten und Bindungskinetiken im Nanomolarbereich ermöglicht wurden. Der Wert dieser Plattform wurde durch die erfolgreiche Entwicklung von kleinen Molekülsonden sowie multivalenten, peptidbasierten Effektoren und fluoreszierenden Sonden für das Abzielen auf ausgewählte Proteine in verschiedenen biologischen Kontexten untermauert. Zusammenfassend hat diese Dissertation eine vielseitige und robuste biophysikalische Plattform für das hochdurchsatz-Studium von PPIs und die Entwicklung von fluoreszierenden Sonden und Inhibitoren für die spezifische Visualisierung und Modulation ausgewählter Zielproteine etabliert.

The global aim of this thesis was to study the use of microarray technology for the screening and identification of biocompatible polymers, to understand physiological phenomena, and the design of biomaterials, implant surfaces and tissue-engineering scaffolds. This work was based upon the polymer microarray platform developed by the Bradley group. Polymer microarrays were successfully applied to find the best polymer supports for: (i) mouse fibroblast cells and used to evaluate cell biocompatibility and cell morphology. Fourteen polyurethanes demonstrated significant cellular adhesion. (ii) Analysis of the adhesion of human erythroleukaemic K562 suspension cells onto biomaterials with particular families of polyurethanes and polyacrylates identified. A DNA microarray study (to access the global gene expression profiles upon cellular binding) demonstrated that interactions between cells and some polyacrylates induced a number of transcriptomic changes. These results suggested that, during these interactions, a chain of cellular changes is triggered, most notably resulting in the downregulation of membrane receptors and ligands. (iii) Identification of polymers with potential applications in the field of stem cell biology. Polymers were identified that showed attachment, promotion and stabilisation of hepatocyte-like cells. A polyurethane support (PU-134) was pinpointed, which significantly improved both hepatocyte-like cell function and “lifespan”. A second project investigated biomaterials that promoted adhesion, growth and function of endothelial progenitor cells. A new polymer matrix was identified which contained the necessary signals to promote endothelial phenotype and function. This has potential application in the creation of blood vessels and the endothelialisation of artificial vessel prostheses and stent coatings for improving angioplasty therapy. (iv) The study of bacterial adhesion, focusing on the adhesion of food-borne pathogenic bacterium Salmonella enterica serovar typhimurium, strain SL1344, and the commensal bacterium Escherichia coli, strain W3110. Several polymers were found to support selective bacterial enrichment, as well as others that minimised bacterial adhesion.

O objetivo deste trabalho é apresentar uma técnica automática baseada em morfologia matemática para medida de sinal em imagens de cDNA desenvolvida no BIOINFO,em parceria com o Instituto Ludwig de Pesquisa contra o Câncer. A tecnologia de lâminas de cDNA é um processo baseado em hibridização que possibilita observar a concentração relativa de mRNA de amostras de tecidos analisando a luminosidade de sinais fluorescentes ou radioativos. Hibridização é o processo bioquímico onde duas fitas de ácido nucleico com seqüências complementares se combinam. A técnica apresentada permite o cálculo da expressão gênica com alto grau de automação, podendo o usuário corrigir com facilidade eventuais erros de segmentação. O usuário interage com o programa apenas para selecionar as imagens e inserir os dados de geometria da lâmina. A estratégia de solução usada tem três fases: gradeamento dos blocos, gradeamento dos spots e segmentação dos spots. Todas as fases utilizam filtros morfológicos e as fases de gradeamento possuem um passo final de correção baseado nos dados de geometria da lâmina o que aumenta a robustez do processo, que funciona bem mesmo em imagens ruidosas. / The objective of this work is to present the automated technique for measuring signal from cDNA images developed in BIOINFO, associated with the Ludwig Institute for Cancer Research. Microarray technology is a hybridization based process that makes possible to quantify the relative abundance of mRNA in two tissue samples analysing the luminosity of fluorescent or radioactive signals. Hybridization is a biochemical process where a strand of nucleic acid matches up its counterpart. The developed technique permits the calculation of gene expression with a high level of automation. Besides that, the user can easily correct eventual segmentation mistakes. The user interacts with the program only to select the images and to set the slide geometry parameters. The solution strategy has three main steps: subarray griding, spots gridding and spots detection. All the steps use morphological filters, and the two gridding steps have a final correction substep based on the slide geometry, increasing the process robustness, that works well even in noisy images.

Neste trabalho desenvolvemos um novo algoritmo para clustering para dados de expressão gênica. As abordagens tradicionais utilizam um conjunto de dados na forma de uma tabela de duas dimensões, onde as linhas são os genes e as colunas são as condições experimentais. Nós utilizamos uma estrutura de três dimensões, acrescentando fatias de tempo. Implementamos nosso algoritmo e testamos com conjuntos de dados sintéticos e dados reais, usando índices de validação para comparar os resultados obtidos pelo nosso algoritmo com os resultados produzidos pelo algoritmo TriCluster. Os resultados mostraram que o nosso algoritmo é bom para dados de expressão gênica em três dimensões e pode ser aplicado a dados de outros domínios / In this study we developed a new clustring algorithm for gene expression data. Previous solutions use a dataset in the form of a table, where the rows are the genes and the columns are the experimental conditions. We used a three-dimensional structure adding time-slices. We implemented this algorithm and tested it with synthetic and real data, using validation index to compare our results with the results obtained by the TriCluster algotithm. Results show that our solution is good for three dimensional gene expression data and can be employed to other domains

O objetivo deste trabalho é apresentar uma técnica automática baseada em morfologia matemática para medida de sinal em imagens de cDNA desenvolvida no BIOINFO,em parceria com o Instituto Ludwig de Pesquisa contra o Câncer. A tecnologia de lâminas de cDNA é um processo baseado em hibridização que possibilita observar a concentração relativa de mRNA de amostras de tecidos analisando a luminosidade de sinais fluorescentes ou radioativos. Hibridização é o processo bioquímico onde duas fitas de ácido nucleico com seqüências complementares se combinam. A técnica apresentada permite o cálculo da expressão gênica com alto grau de automação, podendo o usuário corrigir com facilidade eventuais erros de segmentação. O usuário interage com o programa apenas para selecionar as imagens e inserir os dados de geometria da lâmina. A estratégia de solução usada tem três fases: gradeamento dos blocos, gradeamento dos spots e segmentação dos spots. Todas as fases utilizam filtros morfológicos e as fases de gradeamento possuem um passo final de correção baseado nos dados de geometria da lâmina o que aumenta a robustez do processo, que funciona bem mesmo em imagens ruidosas. / The objective of this work is to present the automated technique for measuring signal from cDNA images developed in BIOINFO, associated with the Ludwig Institute for Cancer Research. Microarray technology is a hybridization based process that makes possible to quantify the relative abundance of mRNA in two tissue samples analysing the luminosity of fluorescent or radioactive signals. Hybridization is a biochemical process where a strand of nucleic acid matches up its counterpart. The developed technique permits the calculation of gene expression with a high level of automation. Besides that, the user can easily correct eventual segmentation mistakes. The user interacts with the program only to select the images and to set the slide geometry parameters. The solution strategy has three main steps: subarray griding, spots gridding and spots detection. All the steps use morphological filters, and the two gridding steps have a final correction substep based on the slide geometry, increasing the process robustness, that works well even in noisy images.

Neste trabalho desenvolvemos um novo algoritmo para clustering para dados de expressão gênica. As abordagens tradicionais utilizam um conjunto de dados na forma de uma tabela de duas dimensões, onde as linhas são os genes e as colunas são as condições experimentais. Nós utilizamos uma estrutura de três dimensões, acrescentando fatias de tempo. Implementamos nosso algoritmo e testamos com conjuntos de dados sintéticos e dados reais, usando índices de validação para comparar os resultados obtidos pelo nosso algoritmo com os resultados produzidos pelo algoritmo TriCluster. Os resultados mostraram que o nosso algoritmo é bom para dados de expressão gênica em três dimensões e pode ser aplicado a dados de outros domínios / In this study we developed a new clustring algorithm for gene expression data. Previous solutions use a dataset in the form of a table, where the rows are the genes and the columns are the experimental conditions. We used a three-dimensional structure adding time-slices. We implemented this algorithm and tested it with synthetic and real data, using validation index to compare our results with the results obtained by the TriCluster algotithm. Results show that our solution is good for three dimensional gene expression data and can be employed to other domains

Les mammites sont des inflammations de la mamelle provoquées principalement par des bactéries. Elles représentent un problème majeur en élevage laitier. Elles sont caractérisées par de fortes augmentations de la concentration des cellules somatiques (CCS) dans le lait. Le score des CCS (SCS) est fortement corrélé à la présence d'infections mammaires, ainsi il est utilisé en sélection pour améliorer la résistance aux mammites. Pour comprendre les mécanismes mis en place lors d'une sélection sur les SCS, deux lignées divergentes de brebis ont été produites à partir de géniteurs ayant des valeurs extrêmes de cet index. Le principal objectif de ce travail est d'identifier et de comprendre les mécanismes qui confèrent une plus grande résistance ou sensibilité aux mammites provoquées par des staphylocoques. Nos travaux ont porté principalement sur des analyses transcriptomiques de trois types cellulaires majeurs, après contact avec des staphylocoques : les cellules inflammatoires du lait recueillies après infection, principalement composées de neutrophiles, les cellules dendritiques, comme cellules présentatrices d'antigènes et les cellules épithéliales mammaires, première barrière de défense contre l'infection. Nous avons montré que la migration des cellules immunitaires et les processus inflammatoires se traduisent par des activations de voies différentes chez les brebis des deux lignées divergentes. Nous avons aussi identifié des gènes fonctionnels candidats pour expliquer la différence de sensibilité aux mammites. En parallèle, nous avons étudié l'association entre le polymorphisme de l'ADN et la présence d'abcès mammaires liés aux mammites dans un dispositif cas-contrôle visant à détecter des gènes à effet majeur. Les études ont permis de mettre en évidence une zone chromosomique d'intérêt située sur le chromosome OAR5. Les deux approches de génétique moléculaire (analyses du transcriptome et du polymorphisme du génome) ont permis d'identifier des gènes fonctionnels et positionnels candidats pour comprendre les mécanismes associés à la résistance aux mammites / Mastitis is udder inflammation. It is one of the major health issues in dairy cattle and sheep as they produce economic losses for the dairy industry. Mastitis is characterized by an increase in milk inflammatory somatic cells. The somatic cell score (SCS) is well correlated to clinical and subclinical mastitis. To enhance insights into the genetic mechanisms involved in such a selection on SCS, two divergent lines of sheep were generated based on extreme breeding values for SCS. The main objective of this work was to identify and improve the understanding of mechanisms that are responsible for a better resistance to Staphylococcus mastitis. Our study was based on transcriptomic analysis of three cell types after Staphylococcus contact: milk inflammatory cells, mainly composed of neutrophils, dendritic cells and mammary epithelial cells. We showed that immune cell migration and inflammatory processes were activated by different pathways in the two divergent lines. We also identified functional candidate genes that may partly explain the differences of mastitis susceptibility. In parallel, we analysed the association of DNA polymorphism with the presence of mammary abscesses caused by mastitis. The study highlighted a chromosomal region on OAR5 that is associated to the presence of mammary abscesses. Both approaches of molecular genetics as transcriptomics and genomics analyses enabled identification of candidate functional and positional genes for the better understanding of the mechanisms associated to mastitis resistance

<p>Streptococcus pneumoniae is a gram-positive bacterium that can be found in both healthy carriers as well as in people that have developed disease. One of the major virulence factors of pneumococci is their polysaccharide capsule. Based on the capsule that surrounds the bacteria, pneumococci are divided into at least 90 different serotypes. Some serotypes seem to be more related to virulence than others.</p><p>I have with comparative genome hybridization microarray technique, studied gene differences between 18 epidemiological well-characterised pneumococcal strains with different potential to cause disease. A microarray chip based on two sequenced pneumococcal genomes, R6 and TIGR4, has already been designed. According to Hierarchical clustering, both the serotype and the genetic type as assessed by MLST (sequence type or ST) seem to have impact on the relationship of clinical isolates. Most clinical isolates of the same serotype are clustered together except for serotype 14 isolates that seem to be more divergent. Further more the number of genes that are divergent between clinical isolates compared to R6 and TIGR4 differ from 65 to 289. Preliminary results indicate that although there is diversity among clinical isolates some are more closely related to each other then others. Absent genes seem to be evenly distributed among all 18 clinical isolates tested but hypothetical genes and genes for cell envelope are two groups of role categories that are absent to the largest extent in all isolates.</p><p>According to results from microarray analysis, a gene region, spr0112-spr1015- is present in all type 9V isolates and absent in many isolates of serotype 14, 19F and 7F. These results have been confirmed by polymerase chain reaction (PCR).</p><p>Conserved genes in a region around the capsule genes have been sequenced to identify marker genes for a capsulular switch between serotype 9V and 14. Preliminary results of the sequencing showed that as much as 750kb might have been transferred in the event of capsular switch.</p>

No description available.

Influenza infection remains constant threat to human health and results in huge financial loss every year. Rapid and accurate detection of influenza can help governments and health organizations monitor influenza activity and take measurements when necessary. In addition, influenza detection in a timingly manner can help doctors make diagnosis and provide effective treatment. On the other hand, novel inhibitors of influenza virus are in high demand because circulating strains have started to develop resistance to currently available anti-viral drugs. Influenza virus has two surface glycoproteins: hemagglutinin (HA) and neuraminidase (NA), which play important roles in the influenza infection. The binding of HA to sialic acid-containing carbohydrates on cell surface initiates virus internalization, while cleavage of terminal sialic acid by NA facilitates viral particle release. In this dissertation, we focus on the development of glycan microarray that is comprised of a panel of NA resistant sialosides, and demonstrate the application of microarray to capture influenza virus at ambient temperature without the addition of NA inhibitors. We also describe a novel electrochemical biosensor for the detection of influenza virus. In addition, we have developed a new class of bivalent NA inhibitors that show promising inhibitory activities against influenza viruses.