Academic literature on the topic 'Genotype-phenotype mapping'

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<br>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

Two studies were conducted on gene mapping analysis. For the first study, genetic simulation experiments were conducted to address the effects of marker density, method of mapping analysis, and gaps in a marker map on the efficiency of QTL detection and the accuracy of QTL parameter estimation. The simulated genome consisted of seven chromosomes with seven or eight segregating QTL affecting the simulated quantitative trait. A set of six randomly segregating QTL outside the test region was consistently used to represent 40% of phenotypic variation. An individual QTL or a linkage block of two QTL on a target chromosome contributed 10% of phenotypic variation. The marker map was either dense (with markers every 4 cM) or sparse (with markers every 20 cM). The gap in the marker map was either 32 cM or 56 cM. Interval mapping and composite interval mapping were used to map QTL on the target chromosome. A dense map provided more power of QTL detection, better accuracy of QTL parameter estimation, and higher false-positive error rates for the target chromosome than a sparse map. Composite interval mapping provided more power of QTL detection, better accuracy of QTL parameter estimation, and lower false-positive error rates than interval mapping. Presence of a large gap in a marker map affected QTL detection and QTL parameter estimation for a QTL inside or near the gap. The use of a dense map with composite interval mapping was the most efficient combination tested in this study. For the second study, a mixed factorial regression model for interval mapping was developed for conducting QTL-by-environment interaction analysis and for providing inferences about QTL that are applicable beyond the environments used in the experiments. Genetic simulation was used to test the model for the power of detecting QTL-by-environment interaction and identifying the types of such interaction as crossover or non-crossover, and for the accuracy of estimating QTL parameters. The model prov

<i>Arabidopsis thaliana </i>(L.)Heyn is a mostly Eurasian species of Brassicaceae with a rosette habit during the vegetative phase. At preliminary experiments, it has been observed that variation in rosette morphology in the juvenile stage, from seedling to flowering, is ecotype and environment specific phenotype. I have examined the genetic basis of rosette architecture putting together whole-rosette high-throughput phenotyping and "phenotype to genotype mapping". Each rosette has been measured for Shape Descriptors derived from Digital Geometry using Computer Vision. Shape Descriptors have been use as traits for Association Mapping (GWAS) and Linkage mapping in three experimental populations: Natural Accessions, Recombinant Inbred Lines derived of a Cape Verde Island x Argentat cross, and Recombinant Inbred Lines from a Multiparent Advanced Generation Intercross (MAGIC). GWAS and Linkage mapping found four potential QTLs during an initial scan. From MAGIC fine-mapping population, 41 potential Quantitative Trait Loci were found associated with rosette global architecture. I hypothesized that genes that integrate developmental response to environment (Erecta, PhyB) have influenced the developmental canalization of rosette morphology in juvenile plants.

A predição da variância fenotípica é de grande importância para que os sistemas de produção de bovinos de corte consigam aumentar a rentabilidade otimizando o uso de recursos. Modelos mecanicistas dinâmicos do crescimento bovino vêm sendo utilizados como ferramentas de suporte à tomada de decisão em sistemas de manejo individual do gado. Entretanto, a aplicação desses modelos ainda fundamenta-se em parâmetros populacionais, sem qualquer abordagem para que se consiga capturar a variabilidade entre sujeitos nas simulações. Assumindo que modelos mecanicistas sejam capazes de simular o componente de desvio ambiental da variância fenotípica e considerando que marcadores SNPs possam predizer o componente genético dessa variância, esse projeto objetivou evoluir em direção a um modelo matemático que considere a variabilidade entre animais em seu nível genético. Seguindo conceitos de fisiologia genômica computacional, nós assumimos que a variância genética da característica complexa (i.e. produto do comportamento do modelo) surge de características componentes (i.e. parâmetros dos modelos) em níveis hierárquicos mais baixos do sistema biológico. Esse estudo considerou dois modelos mecanicistas do crescimento de bovinos - Cornell Cattle Value Discovery System (CVDS) e Davis Growth Model (DGM) - e ao questionar se os parâmetros de tais modelos mapeariam regiões genômicas que englobam QTLs já descritos para a característica complexa, verificou as suas interpretações biológicas esperadas. Tal constatação forneceu uma prova de conceito de que os parâmetros do CVDS e do DGM são de fato fenótipos cuja interpretação pode ser confirmada através das regiões genômicas mapeadas. Um método de predição genômica foi então utilizado para computar os parâmetros do CVDS e do DGM. Os efeitos dos marcadores SNPs foram estimados tanto para os parâmetros quanto para os fenótipos observados. Nós buscamos qual o melhor cenário de predição - simulações dos modelos com parâmetros computados a partir das informações genômicas ou predição genômica conduzida diretamente nos fenótipos complexos. Nós encontramos que enquanto a predição genômica dos fenótipos complexos pode ser uma melhor opção em relação aos modelos de crescimento, simulações conduzidas com parâmetros obtidos a partir de dados genômicos estão condizentes com simulações geradas com parâmetros obtidos a partir de métodos regulares. Esse é o principal argumento para chamar atenção da comunidade científica de que a abordagem apresentada nesse projeto representa um caminho para o desenvolvimento de uma nova geração de modelos nutricionais aplicados capazes de capturar a variabilidade genética entre bovinos de corte confinados e produzir simulações com variáveis de entrada específicas de cada genótipo. Esse projeto é a primeira abordagem no Brasil conhecida dos autores a usar genótipos Bos indicus para o estudo da aplicação de genômica integrada à modelos mecanicistas para o manejo e comercialização de animais na pecuária.<br>The prediction of phenotypic variance is important for beef cattle operations to increase profitability by optimizing resource use. Dynamic mechanistic models of cattle growth have been used as decision support tools for individual cattle management systems. However, the application of such models is still based on population parameters, with no further approach to capture between-subject variability. By assuming that mechanistic models are able to simulate environmental deviations components of phenotypic variance and considering that SNPs markers may predict the genetic component of this variance, this project aimed at evolving towards a mathematical model that takes between-animal variance to its genetic level. Following the concepts of computational physiological genomics, we assumed that genetic variance of the complex trait (i.e. outcome of model behavior) arises from component traits (i.e. model parameters) in lower hierarchical levels of biological systems. This study considered two mechanistic models of cattle growth - Cornell Cattle Value Discovery System (CVDS) and Davis Growth Model (DGM) - and verified their expected biological interpretation by asking whether model parameters would map genomic regions that harbors QTLs already described for the complex trait. This provided a proof of concept that CVDS and DGM parameters are indeed phenotypes whose expected interpretations may be stated by means of their mapped genomic regions. A method of genomic prediction to compute parameters for CVDS and DGM was then used. SNP marker effects were estimated both for their parameters and observed phenotypes. We looked for the best prediction scenario - model simulation with parameters computed from genomic data or genomic prediction on complex phenotypes directly. We found that while genomic prediction on complex phenotypes may still be a better option than predictions from growth models, simulations conducted with genomically computed parameters are in accordance with those performed with parameters obtained from regular methods. This is the main argument to call attention from the research community that this approach may pave the way for the development of a new generation of applied nutritional models capable of representing genetic variability among beef cattle under feedlot conditions and performing simulation with inputs from individual\'s genotypes. To our knowledge, this project is the first of this kind in Brazil and the first using Bos indicus genotypes to study the application of genomics integrated with mechanistic models for the management and marketing of commercial livestock.

Malgré les progrès réalisés dans le séquençage de l’ADN, nous n’avons pas encore compris comment le phénotype d’un organisme se rapporte au contenu de son génome. Cependant, il est devenu clair que l'impact des gènes dépend du contexte. La simple présence d'un gène dans un génome ne nous informe pas du moment où il est exprimé et des autres gènes qui y sont exprimés. Comprendre comment l'expression des gènes est régulée est un élément nécessaire pour comprendre comment les phénotypes émergent d'un génotype donné. Les facteurs de transcription, qui peuvent activer ou réprimer l'expression d'un gène, forment un réseau complexe d'interactions entre eux et leurs gènes ciblés. Ce réseau consiste en une hiérarchie de groupes de facteurs de transcription fortement liés, chacun lié à des processus cellulaires distincts. La structure de ce réseau de régulation transcriptionnelle est-elle significative pour la réponse transcriptionnelle d'une cellule? Ici, nous utilisons une protéine de liaison à l'ADN programmable appelée CRISPR (répétitions courtes palindromiques groupées régulièrement) pour perturber l'expression génique des régulateurs globaux au sein du réseau de régulation transcriptionnelle. Ces régulateurs mondiaux régulent de nombreux processus cellulaires distincts et ont de nombreuses cibles génétiques. Le système CRISPR nous permet de perturber ces régulateurs dans toutes les combinaisons possibles, y compris les perturbations d'ordre supérieur avec tous les régulateurs mondiaux potentiellement ciblés perturbés en même temps. Nous enregistrons ensuite à la fois le modèle d'expression du transciptome en utilisant le séquençage de l'ARN et l'adéquation de chaque souche. Nous trouvons que la structure du réseau de régulation augmente la dimensionnalité de la réponse transcriptionnelle plutôt que de la réduire. Cela se traduit par une épistasie importante au-delà des interactions par paires. Cela a des implications sur la façon dont ces réseaux évoluent. L'épistasie par paires que nous trouvons entre les facteurs de transcription globaux repose sur la présence ou l'absence d'autres perturbations. Cela implique que d'autres perturbations pourraient agir comme des mutations de potentialisation. Le nombre de voies d'évolution potentielles augmente avec les épistasies d'ordre élevé, même si cela ne nous dit rien sur la qualité de ces voies. Fait important, les répliques de cette thèse sont toujours en cours et les données présentées ici n’ont pas encore exclu les artefacts expérimentaux<br>Despite advances in DNA sequencing, we have yet to understand how an organism’s phenotype relates to the contents of their genome. However it has become clear that the impact of genes are context dependant. The mere presence of a gene within a genome does not inform us of when it is expressed, and which other genes are expressed along with it. Understanding how gene expression is regulated is a necessary piece of understanding how phenotypes emerge from a given genotype. Transcription factors, which can activate or repress the expression of a gene, form a complex network of interactions between themselves and their targeted genes. This network consists of a hierarchy of groups of strongly connected transcription factors, each relating to distinct cellular processes. Is the structure of this transcriptional regulatory network significant to the transcriptional response of a cell? Here we use a programmable DNA binding protein called CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) to perturb gene expression of global regulators within the transcriptional regulatory network. These global regulators are regulating many distinct cellular processes and have many genetic targets. The CRISPR system allows us to perturb these regulators in all possible combinations, including higher order perturbations with potentially all targeted global regulators perturbed at the same time. We then record both the expression pattern of the transciptome using RNA sequencing, and the fitness of each strain. We find that the structure of the regulatory network increases the dimensionality of the transcriptional response rather than reducing it. This results in significant high order epistasis beyond pair-wise interactions. This has implications for how these networks evolve. The pair-wise epistasis we find between global transcription factors rely on the presence or absence of other perturbations. This implies that other perturbations could act as potentiating mutations. The number of potential evolutionary paths increases with high order epistasis, although this alone tells us nothing about the quality of those paths. Importantly, the replicates for this thesis are still on-going and the data presented here has not yet excluded experimental artefacts