Mitochondria are responsible for many fundamental biological pathways and metabolic processes, including aerobic ATP production by the mitochondrial respiratory chain. In humans, mitochondrial dysfunction can lead to severe disorders of energy metabolism, which are collectively referred to as mitochondrial disorders and affect approximately 1:5,000 individuals. These disorders are clinically heterogeneous and can affect multiple organ systems, often within a single individual. Symptoms can include myopathy, exercise intolerance, hearing loss, blindness, stroke, seizures, diabetes, and GI dysmotility. Mutations in over 150 genes in the mitochondrial DNA (mtDNA) and nuclear genome are known to cause mitochondrial diseases and an additional ~1,000 nuclear-encoded mitochondrial proteins have the potential to underlie mitochondrial disorders but have not yet been linked to human disease. As a result, determining a molecular diagnosis for patients with suspected mitochondrial disorders remains a challenge.

Since the completion of the sequencing of the human genome at the turn of the century, genomics has revolutionized the study of biology and medicine by providing high-throughput and quantitative methods for measuring molecular activities. Microarray and next generation sequencing emerged as important inflection points where the rate of data generation skyrocketed. The high dimensionality nature and the rapid growth in the volume of data precipitated a unique computational challenge in massive data analysis and interpretation. Noise and signal structure in the data varies significantly across types of data and technologies; thus, the context of the data generation process itself plays an important role in detecting key and oftentimes subtle signals. In this dissertation, we discuss four areas where contextualizing the data aids discoveries of disease-causing variants, complex relationships in the human microecology, interplay between gene and environment, and genetic regulation of gene expression. These studies, each in its own unique way, have helped made possible discoveries and expanded the horizon of our understanding of the human body, in health and disease.

The aim of this thesis is to apply recently developed technologies for genomic variation analyses, and to ensure quality of the generated information for use in preclinical cancer research. Faster access to a patients’ full genomic sequence for a lower cost makes it possible for end users such as clinicians and physicians to gain a more complete understanding of the disease status of a patient and adjust treatment accordingly. Correct biological interpretation is important in this context, and can only be provided through fast and simple access to relevant high quality data. Therefore, we here propose and validate new bioinformatic strategies for biomarker selection for prediction of response to cancer therapy. We initially explored the use of bioinformatic tools to select interesting targets for toxicity in carboplatin and paclitaxel on a smaller scale. From our findings we then further extended the analysis to the entire exome to look for biomarkers as targets for adverse effects from carboplatin and gemcitabine. To investigate any bias introduced by the methods used for targeting the exome, we analyzed the mutation profiles in cancer patients by comparing whole genome amplified DNA to unamplified DNA. In addition, we applied RNA-seq to the same patients to further validate the variations obtained by sequencing of DNA. The understanding of the human cancer genome is growing rapidly, thanks to methodological development of analysis tools. The next step is to implement these tools as a part of a chain from diagnosis of patients to genomic research to personalized treatment. / <p>QC 20121105</p>

Les myopathies congénitales sont des maladies neuromusculaires dont le diagnostic est établi grâce aux données cliniques, histologiques et génétique. Cependant, le diagnostic génétique est manquant pour la moitié des patients, ce qui suggère de nouveaux gènes impliqués. Le but de mon projet était d'identifier de nouveaux gènes de myopathies congénitales et de valider l'impact des mutations trouvées. En utilisant une stratégie d'analyse de séquençage d'exomes de patients déjà exclus pour les gènes connus, nous avons mis en évidence deux nouveaux gènes impliqués dans les myopathies congénitales. Des mutations récessives dans le gène PYROXD1, codant pour une oxydoréductase, causent une myopathie apparaissant à l'enfance avec des défauts spécifiques en histologie. Grâce à un modèle animal, nous avons montré que les mutations impactaient l'activité enzymatique de la protéine. Des mutations dominantes ou récessives dans le gène CACNA1S causent une myopathie avec un phénotype similaire pour toutes les mutations. Les études fonctionnelles ont montré que les mutations causaient un défaut dans le couplage excitation-contraction. / Congenital myopathies are neuromuscular diseases diagnosed by clinical, histological and genetic data. However, the genetic diagnosis is missing for half of the patients, suggesting new genes involved. The goal of my project was to identify new genes of congenital myopathies and validate the impact of the mutations. Using a strategy of analyzing DNA sequencing of patients already excluded for known genes, we have identified two new genes involved in congenital myopathies. Recessive mutations in the PYROXD1 gene, encoding an oxidoreductase, cause a myopathy with childhood-onset and a histology specific spectra. Functionnal studies showed that the mutations have an effect on the enzymatic activity of the protein. We showed that dominant or recessive mutations in the CACNA1S gene cause a neonatal onset myopathy with a similar phenotype for all found mutations.

Bien que l’hétérogénéité des désordres génétiques nous limite dans l’identification du gène causal avec les approches diagnostiques conventionnelles, le séquençage de l’exome a permis d’accroitre le nombre de diagnostics moléculaires posés récemment. Par contre, le grand nombre de variants identifiés par cette méthode pose un défi significatif dans l’interprétation clinique de ses variants. Nous avons donc élaboré PhenoVar, un logiciel qui intègre les données phénotypiques et génotypiques pour retourner une courte liste de diagnostics potentiels. Nous voulons valider cette approche par phénotype au niveau clinique et montrer qu’elle peut être efficace pour diagnostiquer des patients atteints de maladies génétiques rares. Pour ce faire, le séquençage de l’exome a été effectué sur une cohorte de 51 patients. Ceux-ci présentent des dysmorphismes avec ou sans désordres neurodéveloppementaux dont l’étiologie reste indéterminée après plusieurs analyses conventionnelles. Suite au séquençage de l’exome, un pipeline d’analyse bio-informatique nous a permis de filtrer les variations pour garder seulement les variations rares, codantes, ayant une bonne qualité et pour éliminer les artéfacts de séquençage. Ensuite, pour analyser ces variations filtrées, une analyse manuelle et une analyse avec PhenoVar ont été faites. L’analyse manuelle consiste à regarder manuellement chaque variation pour voir son impact et identifier le diagnostic, sans regarder le phénotype du patient. Puis, Exomiser, un autre logiciel utilisant le phénotype, a été utilisé pour comparer les performances de PhenoVar. En comparaison avec l’analyse manuelle, PhenoVar nous a permis de diminuer de six fois le temps d’analyse et de diminuer de moitié le nombre de diagnostics potentiels. Avec ces deux méthodes, nous avons pu trouver le diagnostic moléculaire de 18 patients, soit un rendement diagnostic de 35%. Il est à noter qu’un diagnostic a été manqué par PhenoVar. Cependant, ce diagnostic a été récupéré en enlevant un filtre au niveau du phénotype. De plus, parmi les diagnostics effectués, 16 (89%) se retrouvent dans les dix premiers rangs de PhenoVar, tandis que seulement 10 (56%) se retrouvent dans les dix premiers rangs d’Exomiser. En conclusion, PhenoVar est supérieur à Exomiser pour trouver un diagnostic dans les dix premiers rangs. De plus, il se compare à l’analyse manuelle tout en diminuant le temps d’analyse et le nombre de variants. / Abstract: Although the heterogeneity of genetic disorders limits our capacity to identify the causal gene with conventional approaches, exome sequencing has increased the diagnostic yield. However, the large number of variants identified by this method poses a significant challenge in their clinical interpretation. Thus, we developed PhenoVar: a software that integrates phenotypic and genotypic data and produces a short list of potential diagnoses. The objective of this study is to validate this phenotype-based approach on a clinical level and show that it can be efficient to diagnose patients with rare genetic disorders. Exome sequencing was performed on a cohort of 51 patients. These presented with dysmorphic features with or without neurodevelopmental disorders of undetermined etiology, following conventional analysis. Following exome sequencing, a bioinformatics pipeline allowed us to filter variations, keeping only rare coding variations harboring high quality. Then, we analysed these filtered variations with both manual analysis and PhenoVar. In the manual analysis each variant was manually examined to determine its impact and to identify the diagnosis without taking the patient’s phenotype into consideration. Then, Exomiser, another phenotype-based tool, was used to compare PhenoVar’s performances. In comparison to the manual analysis, PhenoVar has allowed us to reduce the analysis time by six-fold and to reduce by half the number of potential diagnoses. With both methods, we found the molecular diagnosis in 18 patients; a rate of 35%. Moreover, among these diagnoses, 16 (89%) are found in the top 10 ranks of PhenoVar, compared to only 10 (56%) for Exomiser. In conclusion, PhenoVar proved to Exomiser in prioritizing the correct diagnosis in the top 10 ranks. Finally, diagnostic yield of PhenoVar is comparable to the manual analysis while reducing the analysis time and the number of variants.

Lower motor neuron diseases and peripheral neuropathies are two groups of diseases that include multiple rare disorders where many causes are unknown and definitive treatments are unavailable. Understanding the molecular etiology of these genetic diseases provides an opportunity for rapid diagnosis, preconception genetic counseling and, in a subset, direction for the development of future treatment options. The recent introduction of whole exome sequencing (WES) marks a new era in Mendelian genetic disease research as the majority of the coding region of the genome can be sequenced in a timely and cost-effective manner. In this study, WES was used to investigate the molecular etiology of a cohort of 37 patients presenting with lower motor neuron disease or peripheral neuropathy. A molecular diagnosis was determined for seven patients informing the diagnostic utility of WES. Novel phenotypes were found for three genes originally associated with a different disorder. Finally, the foundation has been laid, through the use of functional studies and large scale data-sharing, to identify novel disease-causing genes for lower motor neuron disease and peripheral neuropathy.

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/ Doctorat en Sciences biomédicales et pharmaceutiques (Médecine) / info:eu-repo/semantics/nonPublished

京都大学 / 新制・論文博士 / 博士(医学) / 乙第13442号 / 論医博第2241号 / 新制||医||1054(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 羽賀 博典, 教授 藤田 恭之, 教授 伊藤 貴浩 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

No description available.

Chenopodium quinoa, a South American pseudocereal, has valuable agricultural traits such as salt tolerance and drought tolerance, and it has beneficial nutritional properties such as high protein content and a complete amino acid profile. However, problems including disease susceptibility, low harvest index, lodging, seed shattering, low heat tolerance, and saponin content plague quinoa. Genetic resources for quinoa are needed to fix these problems and make quinoa more available throughout the world. We used ethyl methanesulfonate (EMS) to create a mutant population of QQ74 quinoa (USDA GRIN PI 614886) of 5,030 mutant families. We did whole exome sequencing (WES) on 44 mutant families. Using the recently published quinoa reference genome and MAPS, a mutation detection pipeline, we found a mutation rate of 11.35 mutations/Mb in these families. We also used whole genome sequencing (WGS) to calculate a mutation rate of 21.67 mutations/Mb in an additional nine mutant families. To demonstrate the utility of this population as a genetic resource, we found an EMS-induced nonsense mutation in the betalain synthesis pathway that prevents red betacyanins from accumulating in the hypocotyl of quinoa. With the mutation rates in our population, we calculate that analysis of 300 mutant families will yield 3-7 mutations in any gene of interest, which will facilitate forward and reverse genetic studies in quinoa.