Clustering algorithms and shape factor methods to discriminate among small GTPase phenotypes using DIC image analysis

Naïvement perçu, le processus d’évolution est une succession d’événements de duplication et de mutations graduelles dans le génome qui mènent à des changements dans les fonctions et les interactions du protéome. La famille des hydrolases de guanosine triphosphate (GTPases) similaire à Ras constitue un bon modèle de travail afin de comprendre ce phénomène fondamental, car cette famille de protéines contient un nombre limité d’éléments qui diffèrent en fonctionnalité et en interactions. Globalement, nous désirons comprendre comment les mutations singulières au niveau des GTPases affectent la morphologie des cellules ainsi que leur degré d’impact sur les populations asynchrones. Mon travail de maîtrise vise à classifier de manière significative différents phénotypes de la levure Saccaromyces cerevisiae via l’analyse de plusieurs critères morphologiques de souches exprimant des GTPases mutées et natives. Notre approche à base de microscopie et d’analyses bioinformatique des images DIC (microscopie d’interférence différentielle de contraste) permet de distinguer les phénotypes propres aux cellules natives et aux mutants. L’emploi de cette méthode a permis une détection automatisée et une caractérisation des phénotypes mutants associés à la sur-expression de GTPases constitutivement actives. Les mutants de GTPases constitutivement actifs Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V ont été analysés avec succès.  En effet, l’implémentation de différents algorithmes de partitionnement, permet d’analyser des données qui combinent les mesures morphologiques de population native et mutantes. Nos résultats démontrent que l’algorithme Fuzzy C-Means performe un partitionnement efficace des cellules natives ou mutantes, où les différents types de cellules sont classifiés en fonction de plusieurs facteurs de formes cellulaires obtenus à partir des images DIC. Cette analyse démontre que les mutations Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V induisent respectivement des phénotypes amorphe, allongé, rond et large qui sont représentés par des vecteurs de facteurs de forme distincts. Ces distinctions sont observées avec différentes proportions (morphologie mutante / morphologie native) dans les populations de mutants.  Le développement de nouvelles méthodes automatisées d’analyse morphologique des cellules natives et mutantes s’avère extrêmement utile pour l’étude de la famille des GTPases ainsi que des résidus spécifiques qui dictent leurs fonctions et réseau d’interaction. Nous pouvons maintenant envisager de produire des mutants de GTPases qui inversent leur fonction en ciblant des résidus divergents. La substitution fonctionnelle est ensuite détectée au niveau morphologique grâce à notre nouvelle stratégie quantitative. Ce type d’analyse peut également être transposé à d’autres familles de protéines et contribuer de manière significative au domaine de la biologie évolutive. Evolution is a gradual process that gives rise to changes in the form of mutations that are reflected at the protein level. We propose that evolution of new pathways occurs by switching binding partners, hence creating new functions. The different functions encountered in a given family of related proteins have emerged from a common ancestor that has been duplicated and mutated to become implicated in new interactions and to gain new functions. In this study, we will use native and constitutive active mutant variants of the Ras-like family of small GTPases as working model, to explore such gene duplications, followed by neo / sub-functionalization. The reason for choosing this family resides in the fact that it is a defined set of proteins with well known functions that are mediated through multiple protein-protein interactions.  The aim of this master is to perform a classification of budding yeast phenotypes using different approaches in order to statistically determine at which level of the population these constitutively active mutations are capable to affect cell morphology. Working with a subset of the Ras-like small GTPases family, we recently developed an approach to catalogue and classify these proteins based on multiple physical and chemical criteria. Using microscopic and bioinformatics methods, we characterized phenotypes associated with over-expression of the native small GTPases of the budding yeast Saccharomyces cerevisiae, showing that an established classification is not very clear.  We are interested to investigate how point mutations in small GTPases can affect the cell morphology and their level of impact on asynchronous population. We want to establish a method to determine and quantify mutant and wild type-like phenotypes on these populations using Differential interference contrast microscopy (DIC) images only. As for the first aim of this study, we hypothesize that clustering algorithms can partition mutant cells from wild type cells based on cell shape factor measurements. To prove this hypothesis, we proposed to implement different clustering algorithms to analyze datasets which combines measurements from wild type and respective mutant populations. We created constitutively active forms of these small GTPases and used Cdc42, Rho5, Ras1 and Rsr1 to validate our results. We observed that Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L and Rsr1 G12V mutations induced characteristic amorphous, clumped/elongated, rounded and discrete large phenotypes respectively. This classification allowed us to define a phenotypical classification related to functions. Phenotype classification of the small GTPases has been confirmed using shape factor formulas accompanied with bioinformatics approaches. These approaches which involved different clustering methods allowed an automated quantitative characterization of the phenotypes of up to 7293 mutant cells.  Sequence alignment of Cdc42 and Rho5 showed 46.1% identity as well as 62.6% for Ras1 and Rsr1 allowing the identification of diverged residues potentially involved in specific functions and protein-protein interactions. Directed mutagenesis and substitution of these sites from one gene to another have been performed in some positions to test for specificity and involvement in morphology changes. In parallel, interactions observed for native and constitutively active mutants Cdc42 and Rho5 will be assayed with protein-fragment complementation assay (PCA). This will enable us to determine whether a high correlation exists between functions switches and binding partner’s switches. We propose to expand this approach to the whole Ras-like small GTPases family and monitor protein-protein interactions and functions at a network scale. This research will confirm whether enrichment or depletion of residues in specific sites induces a switch of function due to switching binding partners. Understanding the mechanism underlying such correlation is important to gain insight in the biological mechanisms underlying the Ras-like small GTPases and other proteins evolution. Such knowledge is of fundamental importance in biomedical and pharmaceutical fields, since Ras-like small GTPases represent important targets for therapeutic interventions and for the evolutionary biology field.

Keyphrases: morphologie cellulaire, facteurs de forme cellulaire, algorithmes de partitionnement, cell morphology, shape factors, clustering algorithms, protein-protein interaction networks, petite GTPases Ras