TLE proteins are a closely related family of vertebrate corepressors. They have no intrinsic DNA binding ability, but are recruited as transcriptional repressors by other sequence specific proteins. TLE proteins and their homologues in other species have been implicated in many developmental processes including neurogenesis, haematopoiesis and the formation of major organs. They have also been implicated in early lineage specification in vertebrates but a direct role in this has not been found in mammals. The aim of my PhD is therefore to analyse the function of TLE proteins in early lineage specification and cell fate decisions using mouse embryonic stem cells (ESCs) as a model. The investigation of this has previously been complicated, firstly by the large array of transcription factors that TLEs interact with and secondly by redundancy between similar TLE proteins hindering loss of function approaches. To circumvent these problems, I have used two complementary experimental strategies. The first was identification of point mutations in TLE1 that affect specific classes of DNA binding. Two of these mutations L743F and R534A were of particular interest and were reversibly overexpressed in ES cells to correlate phenotypes to biochemical activity. The second strategy was the mutation of the two primary TLC genes in ES cells and early mouse embryos, TLE3 and TLE4. Complementary evidence from these approaches revealed a role for TLEs in the promotion of ES cell differentiation by repression of pluripotency/self-renewal associated genes. Additionally, neural specification was increased by TLE1 expression especially by the TLE1 point mutations, highlighting opposing roles for negative effects on mesendodermal differentiation. Early mesoderm/primitive streak was increased by loss of TLE, probably through Wnt antagonism. Anterior endoderm was increased by reduced TLE, but a critical level of TLE was still necessary and TLE1 overexpression also upregulated some anterior endoderm markers suggesting both negative and positive roles for TLE proteins in this process.

<p>Malaria is the most common parasitic disease of humans worldwide. A factor that aggravates the many attempts to control the epidemiologic malaria situation is the spreading of resistance against anti-malarial drugs. In this project the point mutation at position 86 of the <em>Plasmodium. </em><em>falciparum</em><em> </em>multidrug resistance gene (<em>pfmdr1</em>), which is thought to contribute to Chloroquine resistance, was analysed in 188 samples from a low transmission area in eastern Sudan, where malaria endemicity is seasonal. The patient group studied had asymptomatic and sub patent parasitemia that persisted during the transmission-free dry season, after being treated with Chloroquine. To differentiate between wild type and mutant genotypes, nested PCR and restriction fragment length polymorphism with the enzyme Apo1 was used. Out of 188 samples 79 (42%) were successfully analysed. Of those, 72% had parasites with mutant genotypes or where mixed infection. No conclusions on the relevance of the <em>pfmd</em><em>r</em><em>1</em> gene in the studied samples are made due to the many remaining gaps. However, eventual sources of error and previous findings in the study area are discussed.</p>

Indivíduos não relacionados apresentam apenas 1% de diferenças entre seus genomas. Estas variações ocorrem na forma de substituições, inserções, deleções, rearranjos complexos ou até estruturais. Dentre essas variações, aquelas que apresentam uma frequência populacional acima de 1% são denominadas de polimorfismos. Tais variações são responsáveis por diferenças que vão desde a resposta imunológica até o tratamento com drogas, incluindo sensitividade das células tumorais, níveis de plasma, efeitos colaterais e toxicidade. A forma mais comum de polimorfismo genético entre humanos são os polimorfismo de base única ou Single Nucleotide Polymorphisms (SNPs), sendo mais de 47 milhões descritos no dbSNP, um banco de dados de pequenos polimorfismos do NCBI. No presente estudo, foi estabelecida uma abordagem computacional, com etapas de exclusão de regiões parálogas ou de baixa qualidade, com o objetivo de identificar variantes genéticas em sequências expressas gerados pelo método de Open Reading Frame ESTs (ORESTES) durante o Projeto Genoma Humano do Câncer. Diferentemente de outros softwares de detecção de polimorfismos, a abordagem computacional descrita neste estudo leva em consideração a informação a priori do número de bibliotecas distintas que reportaram a mesma variação. Foram identificadas 1900 mutações (853 sinônimas e 1047 não-sinônimas) presentes em duas ou mais bibliotecas distintas, que foram validados in-silico contra o dbSNP v130. O resultado da análise identificou 901 mutações já descritas no dbSNP (47,42%). Para confirmação da análise, foram selecionadas 10 mutações (6 novas e 4 já presentes no dbSNP) para validação pelo método de High Resolution Melt (HRM), seguido da caracterização por sequenciamento de DNA. Nesse caso, o resultado foi a validação de 50% das mutações selecionadas. A análise de interação protéica, Protein-Protein Interaction (PPI), realizada com as mutações não-sinônimas localizadas em domínios funcionais, revelou redes gênicas mais complexas em tecidos tumorais do que nos tecidos normais. Esta observação ratificou a literatura a respeito da transformação tumorigênica ser desencadeada pela combinação de mutações que ativam uma série de processos biológicos, para isso, afetando genes, vias gênicas e networks de vias gênicas relacionados. Em resumo, o presente estudo descreve uma abordagem computacional eficiente para identificação de mutações em dados de sequências expressas, além de avaliar o papel das mutações na tumorigênese. / Unrelated humans have only 1% of non-simularity in their genome. These variations occur as substitutions, insertions, deletions, or even complex structural rearrangements. Among these variations, those which show a population frequency above 1% are called polymorphisms. Such variations are responsible for differences ranging from the immune response to treatment with drugs, including sensitivity of tumor cells, plasma levels, toxicity and side effects. The most common form of genetic polymorphism among human are Single Nucleotide Polymorphisms (SNPs), with more than 47 million reported in dbSNP, a database of small polymorphisms from NCBI. In this study, we established a computational approach, with steps to exclude low quality and paralogous regions, aiming to identify genetic variants in expressed sequences generated by the method of Open Reading Frame ESTs (ORESTES) for the Human Cancer Genome Project. Unlike other polymorphisms detection softwares, the computational approach described in this study takes into account the a priori information about the number of different libraries that reported the same variation. We identified 1900 mutations (853 synonymous and 1047 nonsynonymous) present in two or more different libraries, these mutations were in-silico validated against the dbSNP V130. The analysis result showed 901 mutations already described in dbSNP (47.42%). To confirm the analysis, we selected 10 mutations (six new and four already present in dbSNP) for validation by the method of High Resolution Melt (HRM), followed by characterization by DNA sequencing. In this case, the result was the validation of 50 % of the selected mutations. The Protein-Protein Interaction analysis (PPI), performed with non-synonymous mutations located in functional domains, showed more complex gene networks in tumor tissues than in normal tissues. This observation confirmed the literature regarding the tumorigenic transformation is triggered by the combination of mutations that activate a number of biological processes, thereby, affecting genes, gene pathways and networks of related gene pathways. In summary, this study describes an efficient computational approach to identify mutations in expressed sequence data, besides to evaluate the role of mutations in tumorigenesis.

Indivíduos não relacionados apresentam apenas 1% de diferenças entre seus genomas. Estas variações ocorrem na forma de substituições, inserções, deleções, rearranjos complexos ou até estruturais. Dentre essas variações, aquelas que apresentam uma frequência populacional acima de 1% são denominadas de polimorfismos. Tais variações são responsáveis por diferenças que vão desde a resposta imunológica até o tratamento com drogas, incluindo sensitividade das células tumorais, níveis de plasma, efeitos colaterais e toxicidade. A forma mais comum de polimorfismo genético entre humanos são os polimorfismo de base única ou Single Nucleotide Polymorphisms (SNPs), sendo mais de 47 milhões descritos no dbSNP, um banco de dados de pequenos polimorfismos do NCBI. No presente estudo, foi estabelecida uma abordagem computacional, com etapas de exclusão de regiões parálogas ou de baixa qualidade, com o objetivo de identificar variantes genéticas em sequências expressas gerados pelo método de Open Reading Frame ESTs (ORESTES) durante o Projeto Genoma Humano do Câncer. Diferentemente de outros softwares de detecção de polimorfismos, a abordagem computacional descrita neste estudo leva em consideração a informação a priori do número de bibliotecas distintas que reportaram a mesma variação. Foram identificadas 1900 mutações (853 sinônimas e 1047 não-sinônimas) presentes em duas ou mais bibliotecas distintas, que foram validados in-silico contra o dbSNP v130. O resultado da análise identificou 901 mutações já descritas no dbSNP (47,42%). Para confirmação da análise, foram selecionadas 10 mutações (6 novas e 4 já presentes no dbSNP) para validação pelo método de High Resolution Melt (HRM), seguido da caracterização por sequenciamento de DNA. Nesse caso, o resultado foi a validação de 50% das mutações selecionadas. A análise de interação protéica, Protein-Protein Interaction (PPI), realizada com as mutações não-sinônimas localizadas em domínios funcionais, revelou redes gênicas mais complexas em tecidos tumorais do que nos tecidos normais. Esta observação ratificou a literatura a respeito da transformação tumorigênica ser desencadeada pela combinação de mutações que ativam uma série de processos biológicos, para isso, afetando genes, vias gênicas e networks de vias gênicas relacionados. Em resumo, o presente estudo descreve uma abordagem computacional eficiente para identificação de mutações em dados de sequências expressas, além de avaliar o papel das mutações na tumorigênese. / Unrelated humans have only 1% of non-simularity in their genome. These variations occur as substitutions, insertions, deletions, or even complex structural rearrangements. Among these variations, those which show a population frequency above 1% are called polymorphisms. Such variations are responsible for differences ranging from the immune response to treatment with drugs, including sensitivity of tumor cells, plasma levels, toxicity and side effects. The most common form of genetic polymorphism among human are Single Nucleotide Polymorphisms (SNPs), with more than 47 million reported in dbSNP, a database of small polymorphisms from NCBI. In this study, we established a computational approach, with steps to exclude low quality and paralogous regions, aiming to identify genetic variants in expressed sequences generated by the method of Open Reading Frame ESTs (ORESTES) for the Human Cancer Genome Project. Unlike other polymorphisms detection softwares, the computational approach described in this study takes into account the a priori information about the number of different libraries that reported the same variation. We identified 1900 mutations (853 synonymous and 1047 nonsynonymous) present in two or more different libraries, these mutations were in-silico validated against the dbSNP V130. The analysis result showed 901 mutations already described in dbSNP (47.42%). To confirm the analysis, we selected 10 mutations (six new and four already present in dbSNP) for validation by the method of High Resolution Melt (HRM), followed by characterization by DNA sequencing. In this case, the result was the validation of 50 % of the selected mutations. The Protein-Protein Interaction analysis (PPI), performed with non-synonymous mutations located in functional domains, showed more complex gene networks in tumor tissues than in normal tissues. This observation confirmed the literature regarding the tumorigenic transformation is triggered by the combination of mutations that activate a number of biological processes, thereby, affecting genes, gene pathways and networks of related gene pathways. In summary, this study describes an efficient computational approach to identify mutations in expressed sequence data, besides to evaluate the role of mutations in tumorigenesis.

It is important to understand the entire spectrum of somatic variants to gain more insight into mutations that occur in different cancers for development of better diagnostic, prognostic and therapeutic tools. This thesis outlines our work in understanding somatic variant calling, improving the identification of somatic variants from whole genome and whole exome platforms and identification of biomarkers for lung cancer. Integrating somatic variants from whole genome and whole exome platforms poses a challenge as variants identified in the exonic regions of the whole genome platform may not be identified on the whole exome platform and vice-versa. Taking a simple union or intersection of the somatic variants from both platforms would lead to inclusion of many false positives (through union) and exclusion of many true variants (through intersection). We develop the first framework to improve the identification of somatic variants on whole genome and exome platforms using a machine learning approach by combining the results from two popular somatic variant callers. Testing on simulated and real data sets shows that our framework identifies variants more accurately than using only one somatic variant caller or using variants from only one platform. Short tandem repeats (STRs) are repetitive units of 2-6 nucleotides. STRs make up approximately 1% of the human genome and have been traditionally used as genetic markers in population studies. We conduct a series of in silico analyses using the exome data of 32 individuals with lung cancer to identify 103 STRs that could potentially serve as cancer diagnostic markers and 624 STRs that could potentially serve as cancer predisposition markers. Overall these studies improve the accuracy in identification of somatic variants and highlight the association of STRs to lung cancer. / Ph. D.

This thesis describes a computational investigation of the mutagenicity of 5-bromouracil (BrU). In Chapter 1, three models of spontaneous and BrU-induced base mispairing (rare tautomer, wobble pair, and ion) are reviewed. Chapter 2 presents the computational techniques used: electronic structure methods (Hartree–Fock-based and density functional theory) and molecular dynamics. Chapter 3 presents optimisations of the keto and enol tautomers of BrU and uracil (U) in water clusters. The enol tautomer of BrU is found to be more stable than that of U. Chapter 4 is a molecular dynamics study of the keto-enol tautomerism of BrU and U in a periodic water box. The pKₐ of BrU at N3 is found to be lower than that of U. Chapter 5 is a study of stacked base dimers containing BrU, U, or thymine (T) stacking with natural bases. Some structures were taken from the Protein Data Bank, while others were generated using an in-house methodology. BrU is found to stack more strongly than T in vacuo, but solvation and thermal effects nullify this difference. Chapter 6 discusses the significance of the results in Chapters 3–5 in terms of BrU-induced mutagenesis. Appendices A and B–D provide supplementary material to Chapters 2 and 5, respectively. Appendix E is an investigation of the “base flipping” pathway of 2-aminopurine (2AP). Both 2AP/N and A/N dinucleosides (N = thymine or guanine) are found to adopt a wide range of energy-minimum conformations – not only stacked and “flipped”, but also intermediate – and the stacked are not the most favourable by free energy. Appendix F is a list of publications and papers in preparation. One publication concerns BrU stacking. The other is a conformational study of the dipeptide tyrosine-glycine: the theoretical results are shown to be consistent with experiment (R2PI spectra) if thermal effects are taken into account.

Malaria is the most common parasitic disease of humans worldwide. A factor that aggravates the many attempts to control the epidemiologic malaria situation is the spreading of resistance against anti-malarial drugs. In this project the point mutation at position 86 of the Plasmodium. falciparum multidrug resistance gene (pfmdr1), which is thought to contribute to Chloroquine resistance, was analysed in 188 samples from a low transmission area in eastern Sudan, where malaria endemicity is seasonal. The patient group studied had asymptomatic and sub patent parasitemia that persisted during the transmission-free dry season, after being treated with Chloroquine. To differentiate between wild type and mutant genotypes, nested PCR and restriction fragment length polymorphism with the enzyme Apo1 was used. Out of 188 samples 79 (42%) were successfully analysed. Of those, 72% had parasites with mutant genotypes or where mixed infection. No conclusions on the relevance of the pfmdr1 gene in the studied samples are made due to the many remaining gaps. However, eventual sources of error and previous findings in the study area are discussed.

Le maintien de l’homéostasie passe notamment par la sécrétion d’hormones provenant des cellules neuro-endocrines ou endocrines telles que les cellules chromaffines ou les cellules b pancréatiques. Par exemple, la régulation de la glycémie nécessite l’exocytose de l’insuline depuis les cellules b pancréatiques des îlots de Langerhans. Une famille de protéines membranaires est au cœur de la machinerie de fusion d’une vésicule avec la membrane plasmique. Ce groupe appelé, la famille des protéines SNARE est composé de trois protéines. VAMP2 est localisée à la membrane vésiculaire alors que syntaxine 1A et SNAP25 sont localisées à la membrane plasmique. Syntaxine 1A et VAMP2 ont un domaine transmembranaire alors que SNAP25 est reliée à la membrane par prénylation de résidus cystéine. Cette famille forme le complexe cytosolique SNARE décrit comme essentiel à l’exocytose. La structure et la fonction du complexe cytosolique ont été étudiées en profondeur et ont mené au modèle du « zipper ». Celui-ci décrit un enroulement progressif des domaines cytosoliques SNARE permettant l’apposition des membranes puis la fusion. Le rôle des domaines transmembranaires reste encore peu décrit. Pourtant, leur étude est nécessaire afin d’établir un modèle complet de la fusion membranaire par les protéines SNARE. Nous avons donc mené une étude alliant une analyse structurale dynamique à une analyse biologique pour déterminer l’importance du domaine transmembranaire de VAMP2 dans la sécrétion. L’analyse biologique représente donc le centre de ma thèse. Le système biologique utilisé est basé sur l’extinction de l’expression de la protéine VAMP2 endogène et l’expression concomitante d’une protéine VAMP2 mutée dans son domaine transmembranaire. Deux lignées cellulaires considérées comme des modèles dans l’étude de la sécrétion hormonale et du trafic vésiculaire ont servi de support à notre étude. Par des approches de microscopies (confocal, TIRF) et d’analyses biochimiques, nous avons observé les conséquences fonctionnelles des mutations ponctuelles, établis par mutagénèse dirigée, sur le trafic vésiculaire et sur la capacité des cellules à sécréter.Les mutations induites présentent différents effets cellulaires. Certaines bloquent la sortie de VAMP2 du réseau golgien alors que d’autres ont un effet important sur la sécrétion hormonale et plus précisément sur l’exocytose. Les études structurales ont permis de corréler ces effets avec une diminution de la flexibilité structurale dans le cas de la diminution de l’exocytose, ou avec une restriction à la conformation hélice alpha dans le cas du sorting. Ce projet pluridisciplinaire a pu mettre en avant le rôle biologique du domaine transmembranaire de VAMP2 au cours de l’exocytose probablement soutenue par la dynamique conformationelle unique observée par le versant structural du projet. / The hormonal secretion plays a key role in the maintenance of homeostasis. For example, the maintenance of normoglycaemia requires insulin exocytosis from the pancreatic beta cells. The SNARE membrane family protein has been described as the core machinery of fusion between the vesicle containing hormones and the plasma membrane. This family consists of 3 different membrane proteins that are essential during exocytosis. VAMP2 is localized on the vesicle and Syntaxin 1A - on the plasma membrane. They both are transmembrane protein whereas SNAP25 is linked to the plasma membrane by palmitoylation. The SNAREs appear to be essential as they form the cytosolic SNARE complex to dock the vesicle to the plasma membrane. Even though the role of this cytosolic domain has been studied in depth, much less is known on the role of their transmembrane domain during the fusion. Their study remains necessary to establish a complete model of membrane fusion mediated by the SNARE proteins.Here, we have studied the behavior and the role of the SNARE transmembrane domain during exocytosis. In a multidisciplinary project, we have combined a structural approach with a biological study to evaluate the role of this domain. Using mutagenesis in the transmembrane domain of VAMP2 and a cellular system with a clean background, we have assessed the effect of mutations on the secretion and exocytosis in two different cell lines (INS1E and PC12). The biological system is based on the silencing of endogenous VAMP2 and reconstitution of the expression of VAMP2 wt or mutated in the transmembrane domain. Using biochemistry assay and TIRF microscopy we have shown that mutations in this domain can lead to a missorting of the Golgi apparatus or a reduction of the stimulated secretion and exocytosis. This effect can be correlated to a modification of the structural dynamics of this domain.The obtained results clearly demonstrate the role of the transmembrane domain of VAMP2 during exocytosis probably sustained by its unique structural dynamics observed by physico-chemistry.

<p>The ability to accurately predict binding free energies from computer simulations is an invaluable resource in understanding biochemical processes and drug action. Several methods based on microscopic molecular dynamics simulations exist, and in this thesis the validation, application, and development of the linear interaction energy (LIE) method is presented.</p><p>For a test case of several hydrophobic ligands binding to P450cam it is found that the LIE parameters do not change when simulations are performed with three different force fields. The nonpolar contribution to binding of these ligands is best reproduced with a constant offset and a previously determined scaling of the van der Waals interactions.</p><p>A new methodology for prediction of binding free energies of protein-protein complexes is investigated and found to give excellent agreement with experimental results. In order to reproduce the nonpolar contribution to binding, a different scaling of the van der Waals interactions is neccesary (compared to small ligand binding) and found to be, in part, due to an electrostatic preorganization effect not present when binding small ligands.</p><p>A new treatment of the electrostatic contribution to binding is also proposed. In this new scheme, the chemical makeup of the ligand determines the scaling of the electrostatic ligand interaction energies. These scaling factors are calibrated using the electrostatic contribution to hydration free energies and proposed to be applicable to ligand binding.</p><p>The issue of codon-anticodon recognition on the ribosome is adressed using LIE. The calculated binding free energies are in excellent agreement with experimental results, and further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with a ribosome loaded with the Phe UUU codon. The simulations also support the previously suggested roles of A1492, A1493, and G530 in the codon-anticodon recognition process.</p>

The ability to accurately predict binding free energies from computer simulations is an invaluable resource in understanding biochemical processes and drug action. Several methods based on microscopic molecular dynamics simulations exist, and in this thesis the validation, application, and development of the linear interaction energy (LIE) method is presented. For a test case of several hydrophobic ligands binding to P450cam it is found that the LIE parameters do not change when simulations are performed with three different force fields. The nonpolar contribution to binding of these ligands is best reproduced with a constant offset and a previously determined scaling of the van der Waals interactions. A new methodology for prediction of binding free energies of protein-protein complexes is investigated and found to give excellent agreement with experimental results. In order to reproduce the nonpolar contribution to binding, a different scaling of the van der Waals interactions is neccesary (compared to small ligand binding) and found to be, in part, due to an electrostatic preorganization effect not present when binding small ligands. A new treatment of the electrostatic contribution to binding is also proposed. In this new scheme, the chemical makeup of the ligand determines the scaling of the electrostatic ligand interaction energies. These scaling factors are calibrated using the electrostatic contribution to hydration free energies and proposed to be applicable to ligand binding. The issue of codon-anticodon recognition on the ribosome is adressed using LIE. The calculated binding free energies are in excellent agreement with experimental results, and further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with a ribosome loaded with the Phe UUU codon. The simulations also support the previously suggested roles of A1492, A1493, and G530 in the codon-anticodon recognition process.