Rheumatoid arthritis (RA) is an autoimmune disease affecting approximately 1% of the population in the western world. It is characterised by a tissue specific attack of cartilage in peripheral joints. Collagen induced arthritis (CIA) is one of the most commonly used animal models for (RA), with similar symptoms and histopathology. CIA is induced by immunisation of mice with type II collagen (CII), and the immunodominant part was previously found to be located between residues 256-270. This thesis describes the interaction between the MHC molecule, glycopeptide antigens from CII and the T cells that is essential in development of CIA. The glycopeptide properties for binding to the mouse MHC molecule Aq have been studied, as well as interaction points in the glycopeptide that are critical for stimulation of a T-cell response. The thesis is based on five studies. In the first paper the minimal glycopeptide core, that is required for binding to the Aq molecule while still giving a full T cell response was determined. The second paper studied the roles of amino acid side-chains and a backbone amide bond as T-cell contact points. In the third paper the hydrogen bond donor-acceptor characteristics of the 4-OH galactose hydroxyl group of the glycopeptide was studied in detail. In the fourth paper we established a structure activity relationship (QSAR model) for (glyco)peptide binding to the Aq molecule. Finally, the stereochemical requirements for glycopeptide binding to the Aq molecule and for T-cell recognition was studied in the fifth paper. The study was performed using collagen glycopeptide analogues, which were synthesised on solid phase. Amide bond and hydroxyl group isosteres were introduced for study of hydrogen bond donor-acceptor characteristics. Statistical methods were used to design a representative peptide test set and in establishing a QSAR model. The results give a deeper understanding of the interactions involved in the ternary MHC-glycopeptide-T cell complex. This information contributes to research directed towards finding new treatments for RA.

Schizophrenia is a severe psychiatric disorder with unsatisfactory treatment options and poorly under- stood etiology. Genetic models are a suitable tool for studying this disorder with its high heritability. However, currently available animal models do not cover the broad range of schizophrenia symptoms and are not disorder-specific. Ribonucleic acid binding motif protein 12 gene (RBM12), a novel, high- risk gene for schizophrenia, was recently identified. This thesis aimed to assess the social behavior of schizophrenia-like phenotype in RBM12 zebrafish mutants. The social behavior of mutated adult zebrafish was assessed during free-swimming. Trajectories of each zebrafish were obtained from recordings by the usage of idtracker.ai. Parameters selected to quantify the social behavior of the zebrafish were chosen based on common symptoms of humans with schizophrenia. Inter-fish distance was examined as an indicator of preferred personal space since humans diagnosed with schizophre- nia have an increased need for a greater personal space compared to mentally healthy individuals. Wall-hugging, increased speed and bottom-dwelling were studied as indicators of anxiety, a common comorbid symptom of schizophrenia. The RBM12 mutants exhibited a greater inter-fish distance than their wild-type siblings during three-dimensional recordings. They however, did not demonstrate an increased inter-fish distance during two-dimensional recordings. The mutated zebrafish displayed a higher average speed and greater wall-hugging, indicating anxiety. It can be concluded that RBM12 mutation produces partial symptomatology consistent with humans diagnosed with schizophrenia, providing a promising animal model. The current work provided novel insight into the neural substrates of schizophrenia and for potential drug screening for this disorder. Further research is needed to fully characterise schizophrenia-like symptoms in this RBM12 animal model.

No description available.

Gene expression is regulated in response to metabolic necessities and environmental changes throughout the life of a cell. A major part of this regulation is governed at the level of transcription, deciding whether messengers to specific genes are produced or not. This decision is triggered by the action of transcription factors, proteins which interact with specific sites on DNA and thus influence the rate of transcription of proximal genes. Mapping the organisation of these transcription factor binding sites sheds light on potential causal relations between genes and is the key to establishing networks of genetic interactions, which determine how the cell adapts to external changes. In this work I review briefly the basics of genetics and summarise popular approaches to describe transcription factor binding sites, from the most straight forward to finally discuss a biophysically motivated representation based on the estimation of free energies of molecular interactions. Two articles on transcription factors are contained in this thesis, one published (Aurell, Fouquier d'Hérouël, Malmnäs and Vergassola, 2007) and one submitted (Fouquier d'Hérouël, 2008). Both rely strongly on the representation of binding sites by matrices accounting for the affinity of the proteins to specific nucleotides at the different positions of the binding sites. The importance of non-specific binding of transcription factors to DNA is briefly addressed in the text and extensively discussed in the first appended article: In a study on the affinity of yeast transcription factors for their binding sites, we conclude that measured in vivo protein concentrations are marginally sufficient to guarantee the occupation of functional sites, as opposed to unspecific emplacements on the genomic sequence. A common task being the inference of binding site motifs, the most common statistical method is reviewed in detail, upon which I constructed an alternative biophysically motivated approach, exemplified in the second appended article. / QC 20101110

Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. PWMs are compiled from experimentally verified and aligned binding sequences. PWMs are then used to computationally discover novel putative binding sites for a given protein. DNA-binding proteins often show degeneracy in their binding requirement, the overall binding specificity of many proteins is unknown and remains an active area of research. Although PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. A previous study introduced a novel method to PWM training based on the known motifs to sample additional putative binding sites from a proximal promoter area. The core idea was further developed, implemented and tested in this thesis with a large scale application. Improved mono- and dinucleotide PWMs were computed for Drosophila melanogaster. The Matthews correlation coefficient was used as an optimization criterion in the PWM refinement algorithm. New PWMs keep an account of non-uniform background nucleotide distributions on the promoters and consider a larger number of new binding sites during the refinement steps. The optimization included the PWM motif length, the position on the promoter, the threshold value and the binding site location. The obtained predictions were compared for mono- and dinucleotide PWM versions with initial matrices and with conventional tools. The optimized PWMs predicted new binding sites with better accuracy than conventional PWMs.

Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. PWMs are compiled from experimentally verified and aligned binding sequences. PWMs are then used to computationally discover novel putative binding sites for a given protein. DNA-binding proteins often show degeneracy in their binding requirement, the overall binding specificity of many proteins is unknown and remains an active area of research. Although PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. A previous study introduced a novel method to PWM training based on the known motifs to sample additional putative binding sites from a proximal promoter area. The core idea was further developed, implemented and tested in this thesis with a large scale application. Improved mono- and dinucleotide PWMs were computed for Drosophila melanogaster. The Matthews correlation coefficient was used as an optimization criterion in the PWM refinement algorithm. New PWMs keep an account of non-uniform background nucleotide distributions on the promoters and consider a larger number of new binding sites during the refinement steps. The optimization included the PWM motif length, the position on the promoter, the threshold value and the binding site location. The obtained predictions were compared for mono- and dinucleotide PWM versions with initial matrices and with conventional tools. The optimized PWMs predicted new binding sites with better accuracy than conventional PWMs.

Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. PWMs are compiled from experimentally verified and aligned binding sequences. PWMs are then used to computationally discover novel putative binding sites for a given protein. DNA-binding proteins often show degeneracy in their binding requirement, the overall binding specificity of many proteins is unknown and remains an active area of research. Although PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. A previous study introduced a novel method to PWM training based on the known motifs to sample additional putative binding sites from a proximal promoter area. The core idea was further developed, implemented and tested in this thesis with a large scale application. Improved mono- and dinucleotide PWMs were computed for Drosophila melanogaster. The Matthews correlation coefficient was used as an optimization criterion in the PWM refinement algorithm. New PWMs keep an account of non-uniform background nucleotide distributions on the promoters and consider a larger number of new binding sites during the refinement steps. The optimization included the PWM motif length, the position on the promoter, the threshold value and the binding site location. The obtained predictions were compared for mono- and dinucleotide PWM versions with initial matrices and with conventional tools. The optimized PWMs predicted new binding sites with better accuracy than conventional PWMs.

Williams-Beuren syndrome (WBS) is a neurodevelopmental disorder caused by the common deletion of 26-28 contiguous genes in the 7q11.23 region, which poses difficulties to the establishment of genotype-phenotype correlations. The use of mouse models would broader the knowledge of the syndrome, the role of deleted genes, affected pathways and possible treatments. In this thesis project, several mouse models, tissues and cells have been used to define the phenotypes at different levels, the deregulated genes and pathways and to discover modifying elements and novel treatments for the cardiovascular phenotype. In addition, a new binding motif has been described for Gtf2i, a deleted gene encoding a transcription factor with a major role in WB, providing new target genes from deregulated pathways. The obtained results reveal the essential role of mouse models for the study of Williams-Beuren syndrome and provide new treatments options and affected pathways and genes which could be future treatment targets. / La síndrome de Williams-Beuren és una malaltia del neurodesenvolupament causada per una deleció comú d’entre 26 i 28 gens contigus a la regió 7q11.23, dificultant l’establiment de relacions genotip-fenotip. L’ús de models de ratolí pot augmentar el coneixement sobre la malaltia, el paper dels gens delecionats, les vies moleculars afectades i els futurs tractaments. En aquesta tesi s’han usat diversos models de ratolí, les seves cèl·lules i teixits per tal de descriure i definir fenotips, gens i vies moleculars desregulades i per descobrir elements modificadors i nous tractaments. Per últim, s’ha definit un nou motiu d’unió per Gtf2i, uns dels gens delecionats que codifica per un factor de transcripció amb un rol central en la síndrome, proporcionats possible nous gens diana de vies moleculars desregulades. Els resultats obtinguts revelen el paper essencial dels models de ratolí per a l’estudi de la síndrome de Williams-Beuren, proporcionen noves opcions terapèutiques i defineixen nous gens i vies moleculars afectades que podrien suposar noves dianes terapèutiques.