Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [104]-114).

Le cancer est une maladie du génome. La transformation tumorale résulte de l’acquisition de mutations somatiques via divers processus mutagènes opérant tout au long de la vie du patient. Les mécanismes à l’origine des mutations incluent les erreurs de réplication, les défauts de réparation de l’ADN, les modifications de base spontanées ou catalysées par des enzymes cellulaires, et l’exposition à des agents mutagènes endogènes (ROS) ou exogènes (tabac, UV…). Au cours de ma thèse, j’ai analysé des données de séquençage exome et génome complet de tumeurs hépatiques pour décortiquer les mécanismes à l’origine des mutations dans ces tumeurs, leur interaction avec les facteurs de risque, les processus cellulaires, les gènes drivers, et leur évolution au cours de la maladie. J’ai utilisé des méthodes statistiques existantes et dévoloppé des outils bioinformatiques innovants pour:- extraire les signatures de mutations et de réarrangements structuraux à l’aide de données de séquençage à haut débit- identifier les facteurs de risque et/ou les altérations génétiques à l’origine de chacune- prédire les mécanismes mutagènes à l’origine de chaque mutation somatique- explorer les corrélations entre la densité des mutations et les processus cellulaires comme la réplication et la transcription- reconstruire l’histoire clonale des tumeurs et dater l’apparition des signatures mutationnelles et des aberrations chromosomiques.Ces approches innovantes m’ont permis d’identifier 10 signatures mutationnelles: 5 signatures ubiquitaires à l’œuvre dans toutes les tumeurs hépatiques mais modulées par les facteurs de risque (sexe, alcool, tabac), et 5 signatures sporadiques opérant dans moins de 5% des tumeurs et associées à des étiologies connues (aflatoxine B1, acide aristolochique) ou restant à identifier. J’ai aussi mis en évidence 6 signatures de réarrangements structuraux, notamment des phénotypes duplicateurs et déléteurs, spécifiques de petits groupes de tumeurs. Chaque processus mutagène est modulé différemment par la réplication et la transcription. Les signatures liées à des molécules formant des adducts sur l’ADN (hydrocarbures polycycliques aromatiques, aflatoxine B1, acide aristolochique) sont nettement moins actives dans les gènes fortement exprimés suite à l’action du transcription-coupled repair, alors que la signature 16, liée à l’alcool, présente un motif unique de transcription-coupled damage. Une corrélation étonnante entre la densité des petites insertions et délétions (indels) et l’expression des gènes a été identifiée, conduisant à une accumulation considérable d’indels dans les gènes très forterment exprimés dans les cellules hépatiques. Enfin, l’histoire clonale des tumeurs hépatiques montre l’évolution des signatures mutationnelles au cours du temps et identifie l’accumulation de gains chromosomiques multiples comme un évènement tardif entraînant probablement une croissance de la tumeur jusqu’à une taille détactable en clinique. Ces résultats nous éclairent sur les mécanismes à l’origine des altérations génomiques dans l’histoire naturelle des cancers du foie. / Cancer is a disease of the genome. A normal cell goes rogue and is transformed into a cancerous cell due to acquired somatic mutations in its genome. The catalogue of these somatic mutations observed in the cancer genome is the outcome of multiple mutational processes that have been operative over the lifetime of a patient. These mutational processes that have occurred throughout the development of cancer may be infidelity of the DNA replication machinery, impaired DNA repair system, enzymatic modifications of DNA, or exposures to exogenous or endogenous mutagens. Each mutational process leaves a characteristic pattern – a “mutational signature” on the cancer genome. Various genomic features related to genome architecture, including DNA replication and transcription, modulate these mutational processes. During my PhD, I analyzed whole exome and whole genome sequencing data from liver tumors to understand the mutational processes remodeling these tumor genomes, their interaction with risk factors, cellular processes, and driver genes, and their evolution along the tumor histories. For that aim, I used existing statistical methods and I developed innovative computational tools to:- extract mutational and structural variant signatures from next-generation sequencing data- identify risk factors or genetic alterations underlying each process- predict the mutational process at the origin of each somatic mutation- explore correlations between mutation rates and cellular processes like replication and transcription- reconstruct the clonal history of a tumor and the timing of mutational processes and copy-number changes These innovative analytical strategies allowed me to identify 10 mutational signatures: 5 ubiquitous signatures operative in every liver cancer but modulated by risk factors (gender, alcohol, tobacco), and 5 sporadic signatures operative in <5% of HCC and associated with specific known (aflatoxin B1, aristolochic acid) or unknown mutational processes. I also identified 6 structural variant signatures, including striking duplicator or deletor phenotypes in rare tumors. Each mutational process showed a different relationship with replication and transcription. Signatures of bulky DNA adducts (polycyclic aromatic hydrocarbons, aflatoxin B1, aristolochic acid) strongly decreased in highly expressed genes due to transcription-coupled repair, whereas the alcohol-related signature 16 displayed a unique feature of transcription-coupled damage. A striking positive correlation between indel rate and gene expression was observed, leading to recurrent mutations in very highly expressed tissue-specific genes. Finally, reconstructing the clonal history of HCC revealed the evolution of mutational processes along tumor development and identified synchronous chromosome duplications as late events probably leading to fast tumor growth and clinical detection of the tumor. Together, these findings shed new light on the mechanisms generating DNA alterations along the natural history of liver cancers.

No description available.

Avec la révolution moléculaire en biologie, une compréhension des mécanismes de la relation génotype-phénotype est devenue possible. Récemment, les progrès réalisés dans la synthèse et le séquençage de l’ADN ont permis le développement d’expériences de deep-mutational scanning capable de quantifier divers phénotypes pour un ensemble de génotypes sur toute la longueur d’un gène. Ces ensembles de données sont non seulement intéressants en eux-mêmes, mais permettent également de tester de manière rigoureuse des modèles phénotypiques quantitatifs. Nous avons utilisé cette technologie pour caractériser les cartes séquence-fitness de 3 systèmes bactériens modèles: un régulateur global, la CRP, une enzyme de résistance aux antibiotiques, la β-lactamase, et une petite voie métabolique constituée des enzymes AraA et AraB. Ces systèmes ont été choisis pour éclairer les rôles de différentes caractéristiques dans la formation de la relation génotype-fitness (réseaux de régulations, stabilité des protéines et flux métabolique). Nous constatons que la tendance globale des effets sur le fitness semble prévaloir sur les tendances spécifiques. Ceci nous conduit à penser qu’une grande partie de la relation entre le génotype et le fitness pourrait être expliquée à partir de la forme des fonctions de phénotype-fitness. Par ailleurs, nous voyons que la caractérisation de la relation génotype-fitness dans différents systèmes peut être un moyen puissant d’obtenir des informations sur les phénotypes pertinents. / With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep-mutational scanning experiments, capable of scoring comprehensive libraries of genotypes for a variety of phenotypes over the length of entire genes. Such datasets are not only interesting in themselves, but also allow rigorous testing of quantitative phenotypic models. We used this technology to characterise sequence-fitness maps for 3 model bacterial systems: a global regulator, CRP, an antibiotic-resistance enzyme, β-lactamase, and a small metabolic pathway, consisting of the enzymes AraA and AraB. These different systems were chosen to illuminate the roles of different mechanistic features in shaping the genotype-fitness relationship (regulatory wiring, protein stability and metabolic flux). We find that smooth patterns of fitness effects tend to prevail over idiosyncrasy, indicating that much of the genotype-fitness relationship could be understood from the global shape of smooth underlying phenotype-fitness functions. On the flip side, we see that characterising the genotype-fitness relationship in different systems can be a powerful way to glean phenotypic insights.

La relation qui lie la séquence d'une protéine à sa fonction nous échappe toujours en grande partie, pourtant elle est essentielle à la compréhension de l'évolution moléculaire.La microfluidique permet de remplacer les traditionnels tubes à essais par des micro-gouttelettes afin de tester séparément des mutants d'enzyme à des fréquences de l'ordre du kilohertz. Cette technique fournit un moyen de coupler le génotype et le produit de l'activité enzymatique (phénotype). Sélectionner et récupérer les gouttelettes sur demande et séquencer leur contenu permet d'effectuer la cartographie génotype-phénotype de millions de mutants d'enzymes en une seule expérience.Au cours de cette thèse, j'ai tout d'abord développé un système microfluidique basé sur l'expression de protéines in vitro afin de pouvoir réaliser la cartographie génotype-phénotype de Streptomyces griseus aminopeptidase (SGAP). Des gènes mutants de l'enzyme SGAP sont encapsulés (un par gouttelette au maximum) amplifiés, exprimés et testés contre un substrat fluorogénique. Des incompatibilités entre les étapes d'amplification, d'expression et d'essai enzymatique en gouttelettes obligent à réaliser chacune de ces étapes séparément et successivement, afin de diluer le produit de chaque réaction par l'électro-coalescence des gouttelettes. Je montre qu'un work-flow microfluidique dans lequel (i) les gènes sont encapsulés et amplifiés dans des gouttes de 0.2 pL, (ii) exprimés in vitro, (iii) testés contre un substrat fluorogenique dans des gouttelettes de 20 pL, permet de mesurer l'activité de variants de SGAP avec un contraste important. Afin d'optimiser l'essai enzymatique en gouttelettes de SGAP, j'ai aussi développé, en collaboration avec Dr. Johan Fenneteau (Laboratoire de Chimie Organique, ESPCI Paristech), un nouveau substrat fluorogénique basé sur une rhodamine hydrophile. Cette sonde est caractérisée par un échange limité de la rhodamine entre les gouttelettes.J'ai ensuite développé un work-flow microfluidique in vivo, pour Ratus norvegicus trypsin (la trypsine du rat), dans lequel les capacité de sécrétion de Bacillus subtilis sont utilisées afin de simplifier les expériences. Des cellules uniques de B. subtilis sont encapsulées dans des gouttelettes de 20 pL où elles sécrètent des mutants de la trypsine en protéine de fusion avec un rapporteur permettant de mesurer le niveau d'expression. Les mutants sont testés par électro-coalescence avec des gouttelettes de 2 pL contenant un substrat fluorogénique de la trypsine. En normalisant l'activité totale détectée par la fluorescence du rapporteur du niveau d'expression, l'efficacité catalytique peut être directement mesurée en gouttelettes. C'est la première fois qu'un système expérimental d'essai enzymatique haut-débit fournit l'opportunité de mesurer directement l’efficacité catalytique de mutants d'une enzyme à une fréquence de l'ordre du kilo Hertz. Une méthode afin de réaliser la mutagenèse saturée (tous les simples mutants) du gène de la trypsine du rat a aussi été développée. Combinée au séquençage nouvelle génération, la méthode microfluidique développée permettra de réaliser la première cartographie génotype-phénotype de tous les simples mutants de la trypsine du rat / The question of how sequence encodes proteins' function is essential to understand molecular evolution but still remains elusive.Droplet-based microfluidics allows to use micro-metric droplets as reaction vessels to separately assay enzyme variants at the kHz frequency. It also provides an elegant solution to couple the genotype with the product of the catalytic activity of enzymes. Sorting droplets on demand and sequencing their content enables to map the genotype of millions of enzyme variants to their phenotype in a single experiment.First, I developed a cell-free microfluidic work-flow to carry out genotype-phenotype mapping of Streptomyces griseus aminopeptidase (SGAP). Single enzyme variant genes are encapsulated and amplified in droplets, expressed, and assayed against a fluorogenic substrate. Incompatibilities between gene amplification, expression and assay reactions, constrain to execute each one of those steps successively and to dilute the product of each reaction by droplet electro-coalescence. I show that a work-flow in which (i) genes are encapsulated and amplified into 0.2 pL droplets, (ii) expressed using cell-free expression reagents in 2 pL droplets and (iii) assayed with a fluorogenic substrate in 20 pL droplets, allows to measure SGAP variants activity with high contrast. To optimize the SGAP droplet assay, I also developed in collaboration with Dr. Johan Fenneteau (Laboratory of Organic Chemistry, ESPCI Paristech), a hydrophilic rhodamine based substrate, characterized by limited exchange of the released fluorophore between droplets.Second, I developed an in vivo microfluidic work-flow on Ratus norvegicus trypsin (rat trypsin), in which Bacillus subtilis secretion abilities are used to simplify the microfluidic work-flow. Single B. subtilis cells are encapsulated in 20 pL droplets where they secrete trypsin variants as fusion proteins with a fluorescent expression-level reporter. The variants are assayed by droplet electro-coalescence with 2 pL droplets containing a trypsin fluorogenic substrate. Trypsin variants catalytic efficiency can be directly measured in droplets, by normalizing the total trypsin activity by the expression-level reporter fluorescence. This is the first time a high-throughput protein assay work-flow gives the opportunity to directly measure the catalytic efficiency of enzyme variants at the kHz frequency. A method to carry out saturated mutagenesis on the rat trypsin gene was also developed. Together with deep sequencing, the developed experimental work-flow will allow to perform the first quantitative genotype-phenotype mapping of all single point mutants of the rat trypsin protein

Background: In the management of metastatic colorectal cancer (mCRC), KRAS, NRAS and BRAF mutational status individualizes therapeutic options and identify a cohort of patients (pts) with an aggressive clinical course. We hypothesized that relapsed and refractory mCRC pts develop unique mutational signatures that may guide therapy, predict for a response and highlight key signaling pathways important for clinical decision making. Methods: Relapsed and refractory mCRC pts (N=32) were molecularly profiled utilizing commercially available next generation sequencing (NGS) platforms. Web-based bioinformatics tools (Reactome/Enrichr) were utilized to elucidate mutational profile linked pathways-networks that have the potential to guide therapy. Results: Pts had progressed on fluoropyrimidines, oxaliplatin, irinotecan, bevacizumab, cetuximab and/or panitumumab. Most common histology was adenocarcinoma (colon N=29; rectal N=3). Of the mutations TP53 was the most common, followed by APC, KRAS, PIK3CA, BRAF, SMAD4, SPTA1, FAT1, PDGFRA, ATM, ROS1, ALK, CDKN2A, FBXW7, TGFBR2, NOTCH1 and HER3. Pts had on average had >= 5 unique mutations. The most frequent activated signaling pathways were: HER2, fibroblast growth factor receptor (FGFR), p38 through BRAF-MEK cascade via RIT and RIN, ARMS-mediated activation of MAPK cascade, and VEGFR2. Conclusions: Dominant driver oncogene mutations do not always equate to oncogenic dependence, hence understanding pathogenic ` interactome(s)' in individual pts is key to both clinically relevant targets and in choosing the next best therapy. Mutational signatures derived from corresponding ` pathway-networks' represent a meaningful tool to (I) evaluate functional investigation in the laboratory; (II) predict response to drug therapy; and (III) guide rational drug combinations in relapsed and refractory mCRC pts.

Evolutionary dynamics arise from the interplay of mutation and selection. Fundamentally, these two processes operate at different levels: Mutations modify genetic information (the genotype), which is passed from parent to offspring. Selection is triggered by variation in reproductive success, which depends on the physical properties (the phenotype) of an organism and its environment. Thus the genotype-phenotype map determines if and how mutations can lead to selection. The aim of this dissertation is to incorporate this map explicitly into a theoretical description of evolutionary dynamics. The first part of the analysis presented here is concerned with the static properties of simple models of these maps, which are studied using exhaustive enumeration. The two most important observations are phenotypic bias – some phenotypes are realized by many more genotypes than most other phenotypes – and the existence of neutral spaces – genotypes with the same phenotype can often be reached from each other by single mutational steps. The remainder of the dissertation provides a theoretical description of evolutionary dynamics on and across neutral spaces. Two different mean-field approximations lead to simple analytic results for the first discovery of alternative phenotypes, highlighting the importance of phenotypic bias: Rare phenotypes are hard to find by evolutionary search. These results are used to discuss the relationship of robustness, the ability to withstand mutational change, and evolvability, the ability to create variation through mutation. Several types of fluctuations beyond the mean-field limit are studied, both theoretically and in simulations. The discrete structure of genotype spaces can lead to strong correlations in the spectra of phenotypes produced, increasing the probability that a particular phenotype is fixed in the population quickly after its discovery. Structural correlations between genotypes can increase the effect of phenotypic bias, while the qualitative features of the mean-field description remain valid. All these results highlight that neutral spaces impact evolutionary dynamics in many non-trivial ways, in particular by favouring phenotypes of high accessibly, but comparably low fitness over those phenotypes that are highly fit, but very hard to discover.

by Chiu Wing Lok Abe Kurtz. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (p. 218-230). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Hereditary hearing loss is the most common sensory disorder, affecting 1 in 500 newborns. There are more than 538 million individuals with genetic hearing loss worldwide and this number is expected to grow to 1 billion over the next three decades. Currently, the only option for individuals with hearing loss is mechanical intervention such as hearing aids or cochlear implants. In the past decade, many studies have highlighted the need for personalized gene therapy or molecular intervention to treat genetic deafness. However, in order to fulfill this vision a comprehensive understanding of the intricate mutation-gene-phenotype nuances and relationships is required. Toward this goal, we unraveled novel mutation-gene-phenotype associations and mechanisms in four deafness-causing genes (CIB2, COL11A1, CEACAM16 and DFNA5), by using a combination of in-depth phenotyping, human genetics, cutting edge genomic technologies, murine mutant models, and functional assays. These novel insights revealed mutations in CIB2 do not cause Usher Syndrome, mutations in COL11A1 can cause either non-syndromic or syndromic hearing loss, CEACAM16-related deafness is due to two distinct mechanisms, loss of function and gain of function, and coding variants can influence mRNA assembly and cause DFNA5-related hearing loss. Elucidating these novel mutation-gene-phenotype relationships has improved our knowledge of the pathogenic mechanisms underlying hearing loss and provided much needed answers to individuals seeking a diagnosis for their deafness. Recognizing the complexities associated with genetic hearing loss and the challenges in interpreting the clinical significance of genetic variants, we established the first deafness-specific variant database, the Deafness Variation Database (DVD), which classifies over 876,000 variants across 152 deafness-associated genes. This breadth of data provided us with a unique opportunity to explore the molecular landscape of deafness. We show that over 96% of coding variants are rare and novel and that mutational signatures are unique to each gene and are driven by minor allele frequency thresholds, variant effect, and protein domain. The mutational landscape we define shows complex gene-specific variability, making an understanding of these nuances foundational for improved accuracy in variant interpretation. Overall the work presented in this thesis improves our understanding of deafness biology, identifies novel targets for therapeutics and enhances clinical decision-making.

The identification of disease genes (genes that when mutated cause human diseases) is an important and challenging problem. Proper diagnosis, prevention, as well as care for patients require an understanding of disease pathophysiology, which is best understood when the underlying causative gene(s) or genetic element(s) are identified. While the availability of the sequenced human genome helped to lead to the discovery of more than 1,900 disease genes, the rate of disease gene discovery is still occurring at a slow pace. The use of genetic linkage methods have successfully led to the identification of numerous disease genes. However, linkage studies are ultimately restricted by available meioses (clinical samples) which result in numerous candidate disease genes. This thesis addresses candidate gene prioritizations in disease gene discovery as applied toward a genetically heterogeneous disease known as Bardet-Biedl Syndrome (BBS). Specifically, the integration of various functional information and the development of a novel comparative genomic approach (Computational Orthologous Prioritization - COP) that led to the identification of BBS3 and BBS11. Functional data integration and application of the COP method may be helpful toward the identification of other disease genes.