The budding yeast S. cerevisiae is widely used as a model organism to study biological processes that are conserved among eukaryotes. Di fferent genomic approaches have been applied successfully to interrogate the mode of action of small molecules and their combinations. In this thesis, these technologies were applied to di fferent sets of chemical compounds in the context of two collaborative projects. In addition to insight into the mode of action of these molecules, novel approaches for analysis of chemical-genetic pro files to integrate GO annotation, genetic interactions and protein complex data have been developed. The fi rst project was motivated by a pressing need to design novel therapeutic strategies to combat infections caused by opportunistic fungal pathogens. Systematic screens of 1180 FDA approved drugs identifi ed 148 small molecules that exhibit synergy in combination with uconcazole, a widely used anti-fungal drug (Wright lab, McMaster University, Canada). Genome-wide chemical-genetic profiles for 6 of these drugs revealed two di fferent modes of action of synergy. Five of the compounds a ffected membrane integrity; these chemical-genetic interactions were supported by microscopy analysis and sorbitol rescue assays. The sixth compound targets a distinct membrane-associated pathway, sphingolipid biosynthesis. These results not only give insight into the mechanism of the synergistic interactions, they also provide starting points for the prediction of synergistic anti-fungal combinations with potential clinical applications. The second project characterised compounds that aff ected melanocytes in a chemical screen in zebra fish (Patton lab, Edinburgh). Chemical-genetic screens in S.cerevisiae enabled us to show that melanocyte pigmentation reducing compounds do so by interfering with copper metabolism. Further, we found that defects in intracellular AP1 and AP3 trafficking pathways cause sensitivity to low copper conditions. Surprisingly, we observed that the widely-used MAP-kinase inhibitor U0126 a ffects copper metabolism. A nitrofuran compound was found to speci fically promote melanocyte cell death in zebrafi sh. This enabled us to study off -target eff ects of these compounds that are used to treat trypanosome infections. Nifurtimox is a nitrofuran prodrug that is activated by pathogen-specifi c nitroreductases. Using yeast and zebra fish we were able to show that nitrofurans are also bioactivated by host-specifi c aldehyde dehydrogenases suggesting that a combination therapy with an aldehyde dehydrogenase inhibitor might reduce side e ffects associated with nifurtimox.

No description available.

A enorme quantidade de dados que vem sendo gerada por tecnologias modernas de biologia representam um grande desafio para áreas como a bioinformática. Há uma série de programas disponíveis para a análise destes dados, mas que nem sempre são compreendidos o suficiente para serem corretamente aplicados, ou ainda, há problemas que requerem o desenvolvimento de novas soluções. Neste trabalho, nós apresentamos a análise de dados de duas das principais fontes de dados em larga escala: microarrays e sequenciamento. Na primeira, avaliamos se a estatística do método Rank Products (RP) é adequada para a identificação de genes diferencialmente expressos em estudos de doenças complexas, cujo uma das características é a heterogeneidade genética entre indivíduos com o mesmo fenótipo. Na segunda, desenvolvemos uma ferramenta chamada hunT para buscar por genes alvos do fator de transcrição T - um importante marcador de mesoderma com papel chave no desenvolvimento de vertebrados -, através da identificação de sítios de ligação para o T em suas sequências reguladoras. O desempenho do RP foi testado usando dados simulados e dados reais de um estudo de fissura lábio-palatina não-sindrômica, de autismo e também de um estudo que avalia o efeito da privação do sono em humanos. Nossos resultados mostraram que o RP é uma solução eficiente para detectar genes consistentemente desregulados em somente um subgrupo de pacientes, que esta habilidade é mantida com poucas amostras, mas que o seu desempenho é prejudicado quando são analisados poucos genes. Obtivemos fortes evidências biológicas da eficiência do método nos estudos com dados reais através da identificação de genes e vias previamente associados às doenças e da validação de novos genes candidatos através da técnica de PCR quantitativo em tempo real. Já o programa hunT identificou 4.602 genes de camundongo com o sítio de ligação para o domínio do T, sendo alguns deles já demonstrados experimentalmente. Identificamos 32 destes genes com expressão alterada em um estudo onde avaliamos o transcriptoma da diferenciação in vitro de células tronco embrionárias de camundongo para mesoderma, sugerindo a participação destes genes neste processo sendo regulados pelo T / The large amount of data generated by modern technologies of biology provides a big challenge for areas such as bioinformatics. In order to analyze these data there are several computer programs available; however these are not always well understood enough to be correctly applied. Moreover, there are problems that require the development of new solutions. In this work, we present the data analysis of two main high-throughput data sources: microarrays and sequencing. Firstly, we evaluated whether the statistic of Rank Products method (RP) is suitable for the identification of differentially expressed genes in studies of complex diseases, which are characterized by the vast genetic heterogeneity among the individuals affected. Secondly, we developed a tool named hunT to search for target genes of T transcription factor - an important mesodermal marker that plays a key role in the vertebrate development -, by identifying binding sites for T in their regulatory sequences. The RP performance was tested using both simulated and real data from three different studies: non-syndromic cleft lip and palate, autism and sleep deprivation effect in Humans. Our results have shown that RP is an effective solution for the identification of consistently deregulated genes in a subgroup of patients, this ability is maintained even with few samples, however its performance is impaired when only few genes are analyzed. We have obtained strong biological of effectiveness of the method in the studies with real data by not only identifying genes and pathways previously associated with diseases but also corroborating the behavior of novel candidate genes with the real-time PCR technique. The hunT program has identified 4,602 mouse genes containing the binding site for the T domain, some of which have already been demonstrated experimentally. We identified 32 of these genes with altered expression in a study which evaluated the transcriptome of in vitro differentiation of mouse embryonic stem cells to mesoderm, suggesting the involvement of these genes in this process regulated by T

RNA sequencing (RNA-seq) technologies facilitate the characterisation of genes and transcripts in different cell types as well as their expression analysis across various conditions. Due to its ability to provide in-depth insights into transcription and post-transcription mechanisms, RNA-seq has been extensively used in functional genetics and transcriptomics, system biology and developmental biology in animals, plants, diseases, etc. The aim of this project is to use mathematical and computational models to integrate big genomic and transcriptomic data from high-throughput technologies in plant biology and develop new methods to identify which genes or transcripts have significant expression variation across experimental conditions of interest, then to interpret the regulatory causalities of these expression changes by distinguishing the effects from the transcription and alternative splicing. We performed a high resolution ultra-deep RNA-seq time-course experiment to study Arabidopsis in response to cold treatment where plants were grown at 20^oC and then the temperature was reduced to 4^oC. We have developed a high quality <i>Arabidopsis thaliana</i> Reference Transcript Dataset (AtRTD2) transcriptome for accurate transcript and gene quantification. This high quality time-series dataset was used as the benchmark for novel method development and downstream expression analysis. The main outcomes of this project include three parts. i) A pipeline for differential expression (DE) and differential alternative splicing (DAS) analysis at both gene and transcript levels. Firstly, we implemented data pre-processing to reduce the noise/low expression, batch effects and technical biases of read counts. Then we used the limma-voom pipeline to compare the expression at corresponding time-points of 4^oC to the time-points of 20^oC. We identified 8,949 genes with altered expression of which 2,442 showed significant DAS and 1,647 were only regulated by AS. Compared with current publications, 3,039 of these genes were novel cold-responsive genes. In addition, we identified 4,008 differential transcript usage (DTU) transcripts of which the expression changes were significantly different to their cognate DAS genes. ii) A TSIS R package for time-series transcript isoform switch (IS) analysis was developed. IS refers to the time-points when a pair of transcript isoforms from the same gene reverse their relative expression abundances. By using a five metric scheme to evaluate robustly the qualities of each switch point, we identified 892 significant ISs between the high abundance transcripts in the DAS genes and about 57% of these switches occurred very rapidly between 0-6h following transfer to 4^oC. iii) A RLowPC R package for co-expression network construction was generated. The RLowPC method uses a two-step approach to select the high-confidence edges first by reducing the search space by only picking the top ranked genes from an initial partial correlation analysis, and then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association. In future work, we will construct dynamic transcriptional and AS regulatory networks to interpret the causalities of DE and DAS. We will study the coupling and de-coupling of expression rhythmicity to the Arabidopsis circadian clock in response to cold. We will develop new methods to improve the statistical power of expression comparative analysis, such as by taking into account the missing values of expression and by distinguishing the technical and biological variabilities.

Advances in water resources modeling are improving the information that can be supplied to support decisions that affect the safety and sustainability of society, but these advances result in models being more computationally demanding. To facilitate the use of cost- effective computing resources to meet the increased demand through high-throughput computing (HTC) and cloud computing in modeling workflows and web applications, I developed a comprehensive Python toolkit that provides the following features: (1) programmatic access to diverse, dynamically scalable computing resources; (2) a batch scheduling system to queue and dispatch the jobs to the computing resources; (3) data management for job inputs and outputs; and (4) the ability for jobs to be dynamically created, submitted, and monitored from the scripting environment. To compose this comprehensive computing toolkit, I created two Python libraries (TethysCluster and CondorPy) that leverage two existing software tools (StarCluster and HTCondor). I further facilitated access to HTC in web applications by using these libraries to create powerful and flexible computing tools for Tethys Platform, a development and hosting platform for web-based water resources applications. I tested this toolkit while collaborating with other researchers to perform several modeling applications that required scalable computing. These applications included a parameter sweep with 57,600 realizations of a distributed, hydrologic model; a set of web applications for retrieving and formatting data; a web application for evaluating the hydrologic impact of land-use change; and an operational, national-scale, high- resolution, ensemble streamflow forecasting tool. In each of these applications the toolkit was successful in automating the process of running the large-scale modeling computations in an HTC environment.

To identify novel therapeutic targets for basal-like breast cancer (BLBC) subtype, we investigated several DNA repair mechanisms associated with maintenance of high genomic instability for cell survival in cancer cells. We identified that the mismatch repair proteins, MSH2 and MSH6 (referred to as MSH2/6 hereafter), are highly elevated across BLBC samples. High expression level of MSH2/6 in BLBC is associated with worse prognosis and survivability for patients. Therefore, we knocked out MSH2 in BLBC cell lines and performed in vivo xenograft and syngeneic mice model studies to find significant attenuation of tumor growth in MSH2 KO group. Also, MSH2-deficient BLBC cells have increased rate of new mutations. Additionally, we tested the efficacy of conventional chemotherapeutics and radiation treatment that would further tip the genomic instability in MSH2-deficient BLBC cells towards cell death, but found them to be ineffective. Next, we performed high-throughput screening of 1280 FDA-approved compounds to discover that calcium channel blockers preferentially kill MSH2-deficient BLBC cells. This was likely due to association of significantly mutated pathways that involved calcium ion binding and calmodulin binding sites. Here we provide evidence of an alternative therapeutic strategy targeting DNA repair genes in BLBC patients utilizing bioinformatics analysis, high-throughput drug screening, in vitro,and vivoexperimentalmodels.

Regulator of G-protein Signaling (RGS) proteins temporally regulate the G protein signaling cascades initiated by GPCR activation. Reports have established dysregulation of RGS expression in a variety of disease states including several cancers. Additionally, use of genetic ablation techniques has implicated RGS proteins in a variety of other disease states through the native action of the RGS i.e. not a consequence of dysregulation of RGS expression. Therefore identification and optimization of small molecule lead compounds that alter RGS protein function has emerged as a promising therapeutic strategy. In this thesis, we use high throughput screening to interrogate small molecule libraries targeting two RGS proteins, RGS6 and RGS17. RGS6 has been reported as an essential mediator of doxorubicin induced cardiotoxicity, alcohol induced cardio and hepatotoxicity, anxiety, depression, and alcohol dependence. RGS17 has largely been implicated in a variety of cancer pathogenesis, with reported over expression in prostate, lung, breast, and hepatocellular carcinomas. Chapter 2 of this work focuses on the screening efforts targeting RGS6. Three separate screening campaigns interrogating over 20K compounds led to the identification of 3 small molecules that inhibit the RGS6: Gαo protein protein interaction with appreciable selectivity over control assays. The development of a cell based protein interaction assay is discussed, and the compounds were investigated using this system. All compounds tested did not appreciably alter signal over control, meaning that the cellular activity of these compounds remains ambiguous. Chapter 3 details the screening and follow up efforts targeting RGS17. The primary screening and/or follow up of four separate screening campaigns interrogating over 110K compounds is discussed. In total, 10 identified leads and a panel of analogs were subjected to significant follow up evaluation. All compounds were found to be cysteine dependent. The second generation RGS17 inhibitors (UI series) were determined to be both cytostatic and cytotoxic against lung and prostate cancer cell lines in culture, although whether this is due to RGS17 dependent mechanisms or due to general promiscuity of the compounds remains to be determined. Lead compounds from a library provided by the NCI were found to have cellular activity and were subjected to an investigation of structure activity relationships via commercially available compounds. The active form of three of these compounds was found to be a degradation product, which is likely due to decomposition of furan or methyl furan moieties that these compounds shared. One compound demonstrated robust SAR which allowed for the generation of schemes detailing putative inhibitory mechanisms. Finally, the role of RGS17 in the transition from epithelial to mesenchymal phenotypes is investigated. RGS17 was found to cause a sub population of PC3 cells to shift to mesenchymal phenotype, indicating that RGS17 may indeed play a role in this transition. Chapter 4 focuses on efforts to investigate variable potencies of published RGS4 inhibitors against a panel of RGS proteins, with the goal of gleaning insight in to structural characteristics that influence the inhibitability of RGS proteins. Most compounds tested were found to be more potent inhibitors of RGS14 rather than RGS4 in biochemical assays. We developed the NanoBit protein complementation assay to assess the interaction of RGS proteins with either Gαi1 or Gαq in a cellular context, and used this system to investigate compound selectivity in a cellular context. The compounds tested showed selectivity for RGS2, RGS4, and RGS14 over the other RGS proteins tested. The structural differences between the RGS proteins is discussed. Chapter 5 focuses on the future directions the lab may take with respect to the projects outlined in the previous chapters. This includes the screening of more targeted libraries or even virtual screening for RGS6, the development of in vivo assessment tools for RGS17, and an expanded structural examination of RGS proteins including NMR and crystal structure analysis. Additionally, the development of the NanoBit system to interrogate RGS protein interactions that are not RGS: Gα interactions is discussed.

Le niveau médiocre des taux de réponses et des améliorations de survie lorsque des stratégies conventionnelles sont appliquées souligne la nécessité de développer des outils prédictifs performants, robustes et applicables en clinique. La démocratisation des technologies d’analyses à haut-débit est le substrat de la médecine de précision permettant le développement de modèles prédictifs capables d’orienter les stratégies thérapeutiques et la définition d’une nouvelle taxonomie des cancers par l’intégration de données moléculaires de haute dimension. A travers cette thèse, nous avons d’abord analysé des données publiques d’expression génique de cancer bronchique non à petites cellules dans le but de prédire la probabilité de survie à trois ans. Le fort pouvoir prédictif de la TNM seule et la faible taille des cohortes de validation ont malheureusement limité la possibilité de traduire nos résultats en clinique. Nous avons ensuite développé un prédicteur du phénotype « KRAS muté » spécifique du cancer colorectal, permettant d’identifier de nouveaux traits moléculaires responsables de ce phénotype et d’améliorer la prédiction de la réponse au cetuximab chez les patients KRAS sauvage. Enfin, nous avons combiné les données moléculaires des panels de lignées cellulaires CCLE et Sanger avec les données des cohortes du TCGA pour produire des prédicteurs performants de la sensibilité aux drogues. Ces modèles sont concordants avec des screens produits par interférence RNA et permettent d’expliquer la réponse extrême de patients sectionnés dans le programme de screening moléculaire MOSCATO.Les défis spécifiques posés par les données moléculaires de haute dimension dans le développement d’outils prédictifs applicables en clinique sont discutés dans cette thèse. / The mediocre level of the rates of answers and the improvements of survival when conventional strategies are applied underlines the necessity of developing successful, strong and applicable predictive tools in private hospital. The democratization of the technologies of analyses with top-debit(-flow) is the substratum of the medicine of precision allowing the development of predictive models capable of directing the therapeutic strategies and the definition of a new taxonomy of cancers by the integration of molecular data of high dimension(size).Through this thesis(theory), we analyzed at first public data of genic expression of bronchial cancer not in small cells(units) with the aim of predicting the probability of survival in three years. The strong predictive power of the only TNM and

The active human retrotransposon L1 is the most prevalent human retroelement, constituting 17% of the mass of the human genome and contributing significantly to mutagenesis. L1 mutagenizes human genomes in a number of ways including insertional mutagenesis of itself and other retrotransposons, creating of DNA double strand breaks, and induction of non-allelic homologous recombination. Through these processes, the activity of L1 is responsible for approximately 0.5% of all new genetic diseases. All L1-derived mutagenesis stems from the activity of a small number of intact full-length L1 loci that remain capable of mobilization. A smaller subset of these active L1s are called hot L1s and are responsible for the vast majority of all L1 activity. Hot L1s are polymorphic in the population and represent evolutionarily recent L1 insertion events. Here, we show that potentially active full length L1 elements are more prevalent in individual genomes than previously believed. We find that the typical individual likely harbors approximately 60 active and 50 hot L1s. However, we also find that there is significant variation between individuals in numbers of potentially active L1s. As a result, the mutagenic burden associated with L1 likely varies between individuals. / acase@tulane.edu

G–Protein Coupled Receptors are one of the most important targets in drug development, making up over 60% of drug targets. Recent studies have implicated a role of Regulator of G–Protein Signaling (RGS) proteins in the development and progression of pathologies, including some cancers. RGS17, the most–recently identified family member of the RZ family of RGS proteins, has been implicated in the growth, proliferation, metastasis and migration of prostate tumors as well as small–cell and non–small cell lung cancers. In neoplastic tumor tissues RGS17 is up–regulated 13 fold over patient–matched normal tissues in prostate cancer. Studies have shown that RGS17 RNAi knockdown inhibits colony formation and decreases tumorigenesis in nude mice. Based on these findings, this thesis explores the research undertaken to develop small molecule inhibitors of the RGS17: Gαo protein: protein interaction. In this thesis, we implemented AlphaScreen® technology to develop a high–throughput screening method for interrogating small molecule libraries for inhibitors of RGS17. Chapter 3 focuses on the initial results of the AlphaScreen® in 384–well format. The screen utilizes a measurement of the Gα: RGS17 protein: protein interaction (PPI) and with an excellent Z–score exceeding 0.73, a signal to noise ratio >70 and a screening time of 1,100 compounds per hour. Chapter 3 presents the development, validation and initial high–throughput screening for inhibitors of Gα: RGS17 interaction as well as preliminary characterization of the RL series of hits. In this pilot screen the NCI Diversity Set II was interrogated, yielding 35 initial hits of which 16 were confirmed after screening against controls. The 16 compounds exhibited IC50 <10 ΜM in dose–response experiments for inhibiting the Gα: RGS17 interaction. Four exhibited IC50 values <6 ΜM while inhibiting the Gα: RGS17 interaction >50% when compared to a biotinylated GST control (TrueHits). Compounds RL–1 and RL–2 were confirmed by flow cytometry protein interaction assay (FCPIA) while RL–3 and RL–4 were unable to disrupt this PPI in FCPIA. All four compounds were tested using the differential scanning fluorimetry (DSF) method, which is based on energetic coupling between ligand binding and protein unfolding and found compounds RL–1 to RL–4 all slightly increased protein stability upon ligand binding. Chapter 4 focuses on the miniaturization and optimization of AlphaScreen® to a 1536–well format and screening of the MicroSource SPECTRUM and NDL3000 small molecule libraries. This increased throughput 11–fold and decreased our working volumes from 45 ΜL to 10 ΜL, which reduced reagent cost. After optimization, we retained in an excellent Z–factor ≥0.70 with S/N>5.77 and increased the screening rate to more than 12,000 compounds per hour. In this format, the initial screening of the SPECTRUM and NDL3000 libraries was completed and filtered the initial hits by counter screening and PAINs filtering as well as developing four powerful orthogonal assays for the characterization of potential lead molecules. Chapter 6 focuses on the future directions, which include the screening the in–house 50,000 compound library in the University of Iowa HTS Core facility as well as the development of cell based assays to determine the activity of these leads in the cellular milieu. These screens are the first step to developing novel pharmacophores for further optimization of structure with the focus on RGS17 activity in enzymatic, whole cell, xenograft and whole animal models as well as providing new avenues for the development of anticancer therapies.