Transport of intracellular cargo is an important and dynamic process required for cell maintenance and survival. Dynein is the motor protein that carries organelles and vesicles from the cell periphery to the cell center along the microtubule network. Dynactin is a protein that activates dynein for this transport process. Together, dynein and dynactin forms a motor protein complex that is essential for transport processes in all the vertebrate cells. Using fluorescent microscope based live cell imaging techniques and kymograph analyses, I studied dynein/dynactin disruptions on the intracellular transport in two different cell systems. In one set of experiments, effects of dynein heavy chain (DHC) mutations on the vesicular motility were characterized in the fungus model system Neurospora crassa. I found that many DHC mutations had a severe transport defect, while one mutation linked to neurodegeneration in mice had a subtle effect on intracellular transport of vesicles. In a different set of experiments in mammalian tissue culture CAD cells, I studied the effects of dynactin knockdown and dynein inhibition on mitochondrial motility. My results indicated that reductions in dynactin levels decrease the average number of mitochondrial movements and surprisingly, increase the mitochondrial run lengths. Also, I determined that the dynein inhibitory drug Ciliobrevin causes changes in mitochondrial morphology and decreases the number of mitochondrial movements inside cells. Overall, my research shows that distinct disruptions in the dynein and dynactin motor complex alters intracellular motility, but in different ways. So far, my studies have set the ground work for future experiments to analyze the motility mechanism of motor proteins having mutations that lead to neurodegenerative disorders.

No description available.

No description available.

<p>Huntington’s disease (HD) is a devastating autosomal dominant neurodegenerative disorder for which there are no disease modifying treatments. Owing to this are the multiple biological functions of the huntingtin protein and the lack of understanding of the exact pathways being affected in HD. It is clear that the huntingtin protein normally provides anti-apoptotic support and that there are underlying energetic problems and cell stress defects associated with disease. Work from our group has shown that huntingtin acts as a stress sensor and translocates from the endoplasmic reticulum to the nucleus upon cell stress. We therefore hypothesized that huntingtin has a nuclear function in the cell stress response; which would tie together what is currently known about huntingtin, its pro-apoptotic function and the energetic defects of neurodegeneration. In this thesis we describe huntingtin as having a role in the nuclear cofilin-actin rod stress response. Cofilin is an actin binding protein normally involved in actin treadmilling. During stress, cofilin saturates F-actin leading to rod formation which functions to alleviate ATP. We show that this response is impaired in the presence of mutant huntingtin and that the aberrations in this response can be mediated through the enzyme tissue transglutaminase. Little is known about the physiological role and requirement of the cofilin-actin rod response. Therefore we created a system to test if rod formation was required in cells during stress, which indicates if and how targeting this pathway will be possible. We additionally looked at targeting the nuclear import and export properties of the cofilin protein, which directly affect rod formation and may be targetable in cofilin modifying drug discovery efforts. Overall, this work has described a specific and relevant pathway affected by mutant huntingtin and started the process of assessing this pathway as a therapeutic avenue for Huntington’s disease.</p> / Doctor of Philosophy (PhD)

Autophagy is a conserved biological stress response in mammalian cells that is responsible for clearing damaged proteins and organelles from the cytoplasm and recycling their contents via the lysosomal pathway. In cases where the stress is not too severe, autophagy acts as a survival mechanism. In cases of severe stress, it may lead to programmed cell death. Autophagy is abnormally regulated in a wide-range of diseases, including cancer. To integrate the existing knowledge about this decision process into a rigorous, analytical framework, we built a mathematical model of cell fate decision mediated by autophagy. The model treats autophagy as a gradual response to stress that delays the initiation of apoptosis to give the cell an opportunity to survive. We show that our dynamical model is consistent with existing quantitative measurements of time courses of autophagic responses to cisplatin treatment. To understand the function of this response in cancer cells we have provided a systems biology experimental framework to study dynamical aspects of autophagy in single cancer cells using live-cell imaging and quantitative uorescence microscopy. This framework can provide new insights on function of autophagic response in cancer cells. / Ph. D.

The research investigates the sliding motility of Clostridium perfringens by employing machine learning-based image segmentation techniques and tracking to extract key quantitative characteristics of the movement of the bacteria. C. perfringens cells maintain end-to-end connections after cell divisions and form elongated chains that expand in a one-dimensional fashion. Cells in the elongating chains are pushed by each other to achieve a sliding movement at potentially high speeds. However, these chains are susceptible to breakage due to stress accumulation from rapid growth, which would undermine efficiency of the passive sliding motility. Utilizing AI-powered image analysis, this research aims to obtain detailed quantification of these dynamics and generate data for future mechanistic studies of the sliding motility. Results from this work highlight the effectiveness of machine learning in detecting individual cells from microscopy images. The accurately segmented cells enable enhanced tracking and detailed analysis of bacterial motility. The results generate useful quantitative data such as growth rate, velocity, and division frequency of C. perfringens. / Master of Science / The research project explores the movement of Clostridium perfringens, a bacterium often responsible for food poisoning, by using machine learning techniques to observe and analyze how each bacterial cell moves within its colony. These bacteria form long, chain-like structures that help them move more rapidly. However, these chains can break when they become too long and undergo too much stress. By applying artificial intelligence-based tools to automatically detect and track cells in time-lapse microscopy videos, the project provides useful data of how these bacteria slide, grow and divide. These data will help us understand the chain-based bacterial sliding in C. perfringens and its underlying mechanism.

Telomeres are nucleoprotein structures that cap the physical ends of eukaryotic chromosomes. They consist of repetitive DNA sequences 5’-TTAGGG-3’ assembled with proteins which form the shelterin complex. This complex protects the ends of chromosomes by inhibiting DNA repair pathways at telomeres and avoid their recognition as double-strand breaks. Telomeres have been identified as a transcriptionally silent zone until 2007 when a noncoding RNA called TERRA (TElomeric Repeat containing RNA) transcribed from telomeres was discovered. This RNA gave rise to many questions: How is TERRA regulated? How is TERRA expressed? Does TERRA interact with proteins, DNA or RNA? After several studies, we know that TERRA is frequently expressed in cancer cells and it interacts with a large proteome. Nevertheless, its specific function remains unknown. In this thesis, we studied this RNA in human cancer cells using live-cell microscopy which allowed us to get information on TERRA’s dynamics, localization and its interactome. Moreover, we used single-molecule imaging on TERRA 15q labeled by the MS2-GFP system, which allowed the visualization of TERRA transcripts. This study resulted in the discovery of two types of TERRA population from telomere 15q: one of the population is characterized by the formation of clusters and a second population is constituted of unique molecules more dynamic in the nucleus. Finally, in order to better understand TERRA’s functions, we developed a new approach which consists on immunoprecipitating TERRA using the MS2 stem-loops as a tag to identify TERRA-interacting proteins such as the telomeric factor TRF2 or RNA-binding proteins like hnRNP -A1 or FUS. / Les télomères forment les extrémités des chromosomes chez les eucaryotes. Ces séquences répétées en tandem 5’-TTAGGG-3’ font partie d’un complexe nucléoprotéique appelé shelterin. En effet, cet assemblage de protéines télomériques permet la protection des extrémités des chromosomes, permettant à celles-ci de ne pas être reconnues comme des cassures dans l’ADN et d’activer les voies de réparation de l’ADN. Les télomères ont longtemps été reconnus comme étant des zones de transcription inactives, ce jusqu’en 2007 lorsqu’une équipe de recherche découvrit un ARN non codant appelé TERRA (Telomeric Repeat containing RNA). Ce dernier a suscité de nombreuses questions : quel est le rôle de cet ARN? Comment est-il exprimé et régulé? Interagit-il avec d’autres facteurs cellulaires? Les différentes recherches menées sur cet ARN ont permis de conclure que celui-ci était fréquemment induit dans les cellules de cancer, que ses partenaires d’interactions sont nombreux, mais que ses fonctions restent encore mal définies. Par ailleurs, ces différentes études ont toujours été ou presque réalisées sur des cellules fixées, sur une population totale d’ARN télomérique TERRA. Afin d’apporter de nouvelles réponses et de mieux caractériser cet ARN, nous avons étudié ce transcrit dans des cellules de cancer humain en utilisant la technique de microscopie en temps réel, qui permet de récolter des données sur la dynamique, la localisation de cet ARN et ses éventuels partenaires. De plus, nous nous sommes intéressés à des molécules uniques de TERRA issues du télomère 15q en exploitant la technique de marquage avec des tiges-boucles MS2 (MS2-GFP). Cette étude de microscopie a permis de découvrir deux types de population de l’ARN TERRA 15q : une population caractérisée par des assemblages d’ARN dit clusters (agrégats d’ARN) et une population plus singulière qui semble avoir une diffusion plus importante dans le noyau de la cellule. Par ailleurs, l’expression de l’ARN TERRA semble être différente d’un type cellulaire à un autre et nous avons donc cherché à connaître le niveau d’expression de cet ARN au sein de la lignée étudiée au cours de ce projet de recherche. Enfin, afin de découvrir de nouveaux rôles pour cet ARN, nous avons développé une approche de co-immunoprécipitation de l’ARN TERRA pour identifier des interactions avec des protéines du complexe shelterin comme TRF2, ou des protéines liant l’ARN comme hnRNP-A1 ou encore FUS.

Enfermer le porteur de l’information génétique dans le noyau a obligée la cellule a créé un système de transport complexe, qui permet l’export d’un ARNm du noyau au cytoplasme. Le mécanisme général de l’export des ARNm est encore mal connu, même si les facteurs principaux ont été découverts il y a longtemps. De récents progrès en microscopie nous ont permis d’étudier directement le comportement des ARNm durant le processus d’export. Durant ma maitrise, nous avons été capables de localiser et suivre des ARNm en temps réel pour la première fois chez Saccharomyces cerevisiae. Nous avons créé un gène rapporteur en mettant le gène GLT1 sous le contrôle du promoteur GAL1. Nous avons aussi marqué l’ARNm de GLT1 avec plusieurs boucles PP7. L’ARNm sera visible après l’attachement de plusieurs protéines PP7-GFP aux boucles. En utilisant la technique d’imagerie en cellules vivantes, nous sommes capable de visualiser et suivre chaque ARNm, depuis son relâchement du site de transcription jusqu’à l’export. Une fois relâché du site de transcription, l’ARNm diffuse librement dans le nucléoplasme, mais une fois à la périphérie nucléaire, il commence à « scanner » l’enveloppe nucléaire avant d’être exporté. Nous avons trouvé que le « scanning » dépend de la présence des Myosin Like Proteins (Mlp1p et Mlp2p), protéines qui forment le panier nucléaire, car suite à la délétion de MLP1 et MLP2, les ARNm n’étaient plus capable de « scanner ». Nous avons également trouvé que la partie C-terminale de Mlp1p était nécessaire au « scanning ». De plus, suite à la délétion du gène TOM1, gène codant pour une ubiquitine ligase, les ARNm ont un comportement similaire aux ARNm d’une souche ∆mlp1/mlp2, suggérant que le « scanning » permet à Tom1p d’ubiquitiner Yra1p, ce qui causera son relâchement de l’ARNm. Également, nous avons montré que les ARNm endogènes MDN1 et CBL2 scannent aussi la périphérie nucléaire. Ensemble, nos résultats suggèrent que le scanning est un processus par lequel passent tout les ARNm nucléaire lorsqu’ils se retrouvent à la périphérie du noyau, pour initier plusieurs étapes de réarrangements nécessaires à leurs export. De plus, nous avons examiné le rôle de Yhr127p, une protéine nouvellement identifiée qui se lie à l’ARN. Après avoir marqué cette protéine avec la GFP, nous avons montré qu’elle forme des foci dans le noyau et que ces derniers vont disparaitre suite à l’arrêt de la transcription. La délétion de YHR127 à conduit à une augmentation de la transcription de quelques gènes spécifiques, mais n’affecte pas la capacité de la cellule à exporter les ARNm. Nos résultats suggèrent que cette protéine joue un rôle dans la régulation de la transcription et/ou dans la stabilité de l’ARNm. / In eukaryotic cells, the processes of RNA and protein synthesis are spatially separated into two distinct compartments. With this division, a complex pathway of nucleocytoplasmic RNA export has evolved, which to date remains poorly understood. Recent advances in single-molecule microscopy have enabled direct studies focused on investigating the dynamics and kinetics of RNA export. In this Master thesis, we present the first real time visualization of mRNA export in the yeast Saccharomyces cerevisiae. We first generated a GLT1 reporter under the control of the inducible GAL1 promoter, in which the GLT1 mRNA was tagged with an array of PP7 repeats and detected by exogenous PP7-GFP binding protein. Using a single-molecule live cell imaging approach, we were able to visualize and track the behavior of individual mRNAs from the site of transcription to the point of export. Interestingly, we found that once released from the transcription site, single mRNAs diffuse freely in the nucleoplasm, but once they reach the nuclear periphery, they scan the periphery before being exported to the cytoplasm. This scanning behavior was dependent on Myosin Like Proteins (Mlp1p and Mlp2p), which form the basket of the Nuclear Pore Complex (NPC), as mRNAs were not retained at the periphery and were rapidly released into the nucleoplasm in mlp1p/mlp2p double mutant cells. Specifically, we found that the C-terminal part of Mlp1p was important for scanning. Furthermore, mRNAs from cells depleted of the E3 ubiquitin ligase TOM1 had a similar phenotype to mRNAs in mlp1p/mlp2p double mutant cells, suggesting a role for scanning in the Tom1p-mediated release of Yra1p from the RNA. Lastly, we confirmed that endogenous MDN1 and CBL2 mRNAs also exhibit scanning behaviour. Taken together, our results suggest that mRNAs scanning the nuclear periphery is a general behaviour for all mRNAs to initiate the mRNA export process, allowing mRNP arrangement required for export to occur at the nuclear periphery. In addition, we investigated the role of YHR127, a newly identified RNA binding protein, in RNA biogenesis. Notably, we show that GFP-tagged YHR127p formed distinct foci in the nucleus, which were lost upon transcription arrest. Deletion of YHR127 led to an increase in transcript levels of specific genes, but not to a global accumulation of mRNAs in the nucleus, suggesting a role for this protein in regulating transcription and/or mRNA stability.

Dynamische zelluläre Prozesse wie Zellzyklus, Signaltransduktion oder Transkription zu analysieren wird Live-cell-imaging mittels Zeitraffermikroskopie verwendet. Um nun aber Zellabstammungsbäume aus einem Zeitraffervideo zu extrahieren, müssen die Zellen segmentiert und verfolgt werden können. Besonders hier, wo lebende Zellen über einen langen Zeitraum betrachtet werden, sind Fehler in der Analyse fatal: Selbst eine extrem niedrige Fehlerrate kann sich amplifizieren, wenn viele Zeitpunkte aufgenommen werden, und damit den gesamten Datensatz unbrauchbar machen. In dieser Arbeit verwenden wir einen einfachen aber praktischen Ansatz, der die Vorzüge der manuellen und automatischen Ansätze kombiniert. Das von uns entwickelte Live-cell-Imaging Datenanalysetool ‘eDetect’ ergänzt die automatische Zellsegmentierung und -verfolgung durch Nachbearbeitung. Das Besondere an dieser Arbeit ist, dass sie mehrere interaktive Datenvisualisierungsmodule verwendet, um den Benutzer zu führen und zu unterstützen. Dies erlaubt den gesamten manuellen Eingriffsprozess zu rational und effizient zu gestalten. Insbesondere werden zwei Streudiagramme und eine Heatmap verwendet, um die Merkmale einzelner Zellen interaktiv zu visualisieren. Die Streudiagramme positionieren ähnliche Objekte in unmittelbarer Nähe. So kann eine große Gruppe ähnlicher Fehler mit wenigen Mausklicks erkannt und korrigiert werden, und damit die manuellen Eingriffe auf ein Minimum reduziert werden. Die Heatmap ist darauf ausgerichtet, alle übersehenen Fehler aufzudecken und den Benutzern dabei zu helfen, bei der Zellabstammungsrekonstruktion schrittweise die perfekte Genauigkeit zu erreichen. Die quantitative Auswertung zeigt, dass eDetect die Genauigkeit der Nachverfolgung innerhalb eines akzeptablen Zeitfensters erheblich verbessern kann. Beurteilt nach biologisch relevanten Metriken, übertrifft die Leistung von eDetect die derer Tools, die den Wettbewerb ‘Cell Tracking Challenge’ gewonnen haben. / Live cell imaging based on time-lapse microscopy has been used to study dynamic cellular behaviors, such as cell cycle, cell signaling and transcription. Extracting cell lineage trees out of a time-lapse video requires cell segmentation and cell tracking. For long term live cell imaging, data analysis errors are particularly fatal. Even an extremely low error rate could potentially be amplified by the large number of sampled time points and render the entire video useless. In this work, we adopt a straightforward but practical design that combines the merits of manual and automatic approaches. We present a live cell imaging data analysis tool `eDetect', which uses post-editing to complement automatic segmentation and tracking. What makes this work special is that eDetect employs multiple interactive data visualization modules to guide and assist users, making the error detection and correction procedure rational and efficient. Specifically, two scatter plots and a heat map are used to interactively visualize single cells' visual features. The scatter plots position similar results in close vicinity, making it easy to spot and correct a large group of similar errors with a few mouse clicks, minimizing repetitive human interventions. The heat map is aimed at exposing all overlooked errors and helping users progressively approach perfect accuracy in cell lineage reconstruction. Quantitative evaluation proves that eDetect is able to largely improve accuracy within an acceptable time frame, and its performance surpasses the winners of most tasks in the `Cell Tracking Challenge', as measured by biologically relevant metrics.

Mathematical modeling has made great strides since the Lotka-Volterra predator-prey models. Newer models attempt to describe sub-cellular signal transduction pathways, such as the JAK-STAT and NF-κB pathways. However, the tools to accurately determine reaction and translocation rates in these pathways still have a number of drawbacks, including the effects of concentration scale on determining reaction rates and the effects of bulky additions to translocation rates. One method of overcoming these problems in signal transduction rate determination is to sample and stain cells from a full population at specific time points. However, fixed cell methods can only generate an average population rate. This could become an issue if the rate depends on the genotype of one of the proteins in the pathway. Another method of overcoming these problems in signal transduction rates is to use unmarked nuclei in live-cell imaging techniques. However, live cell imaging methods poses different problems, primarily how to find and track nuclei and cytoplasm when cells are actively moving and the nuclear and cytoplasmic intensities are by necessity fluctuating. To date, there is only one software package designed for tracking cells under these conditions - Cell Tracker (Shen et al., 2006). Cell Tracker is designed to handle the tracking of live cell images for protein translocation studies. They recommend using a separate color channel to mark the nucleus, although results can be obtained using unmarked nuclei. The results from Cell Tracker with unmarked nuclei are often less than optimal. We have developed a novel segmentation scheme and variation of the particle filter algorithm to allow more accurate tracking in time series with unmarked nuclei. The proposed segmentation scheme uses a non-parametric level set algorithm to refine a fast initial thresholding step. The tracking scheme uses a dense optical flow calculation to assist the particle filter algorithm in continuing to follow the true positions of the nuclei. To test the proposed algorithm, a novel mimicry of cell movement has been developed using random perturbations of a triangular mesh structure through the use of the finite element method.