The neuropathological process in Alzheimer's disease (AD) is characterized by both intra and extracellular Aβ42 aggregates. The neuropathological process of AD is complex and the exact cause of Aβ aggregation leading towards neuronal death is yet unknown. Several events are implicated towards the development of AD including changes within the proteome. With more than 30 million people currently affected with AD, there is still no cure for AD. In this project we seek to identify differential protein profiles by undertaking a comparative analysis of the intracellular and extracellular effects of Aβ on the human proteome using two cellular neuronal models: MC65 and SHSY5Y cells, to understand the biochemical pathology underlying AD. We also initiated a compound screening approach which not only identified several small molecules and peptides inhibiting the Aβ cytotoxicity, but also identified several known compounds from the LOPAC library acting as potential inhibitors of intra and extracellular Aβ42 cytotoxicity, thus highlighting the importance of drug repositioning to identify novel compounds in the therapeutic regime of AD which could be categorized as Aβ toxicity inhibitors. A comparative qualitative proteomics approach was undertaken using OFFGEL fractionation. The MS data was analysed through GO, biological pathway and protein interaction analysis using various databases such as UniProtKB, DAVID v6.7, KEGG and String 9.0 for the SHSY5Y cells treated with extracellular Aβ42 and MC65 cells which conditionally express intracellular C99, that is further cleaved to intracellular Aβ. This was followed by validation of 8 proteins by in-cell Western assay (ICW) undertaken using the LI-COR Infrared Imaging System for the cell lysates of control and Aβ42 treated SH-SY5Y as well as Aβ induced MC65 cells. We have also screened a library of 1280 LOPAC compounds on both the cell lines and 9 other compounds previously known as Aβ toxicity inhibitors on MC65 cells. The lead compounds were further characterized using MTT, LDH, ThT and ICW assays. The proteomics methodology undertaken through this project identified several novel proteins specific to intracellular and extracellular Aβ aggregation. The GO, biological pathway analysis and the functional interaction study helped to identify proteins associated from the proteasome pathway to be affected as an effect of Aβ aggregation for both the cells exposed with intra and extracellular Aβ aggregation. The compound screening study also identified several compounds as inhibitors of Aβ cytotoxicity. A-77636, a D1 dopamine receptor agonist was identified as a lead compound to reduce the extracellular Aβ42 cytotoxicity at nM concentration. Moreover, 1,3-Diethyl-8-phenylxanthine and Arecaidine propargyl ester hydrobromide also proved successful in attenuating the extracellular Aβ42 cytotoxicity. Apart from this; SEN1000, SEN304 and Scylloinositol were able to completely attenuate the intracellular Aβ cytotoxicity, whereas two other compounds, 1,3-Dipropyl-8-p-sulfophenylxanthine and 3-Bromo-7-nitroindazole from the LOPAC library proved effective in acting as partial inhibitors of intracellular Aβ aggregation induced cytotoxicity. The ADME profile for most of these compounds is acceptable, therefore these can be considered as therapeutic leads for AD in the future.

The problem of developing drugs that can be used to cure diseases is important and requires a careful approach. Since pursuing the wrong candidate drug for a particular disease could be very costly in terms of time and money, there is a strong interest in minimizing such risks. Drug repositioning has become a hot topic of research, as it helps reduce these risks significantly at the early stages of drug development by reusing an approved drug for the treatment of a different disease. Still, finding new usage for a drug is non-trivial, as it is necessary to find out strong supporting evidence that the proposed new uses of drugs are plausible. Many computational approaches were developed to narrow the list of possible candidate drug-target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all non-cyclic paths that connect a drug and a target, and using a function that we define, calculates a score from all the paths. This score describes our confidence that DTI is correct. We show that DASPfind significantly outperforms other state-of-the-art methods in predicting the top ranked target for each drug. We demonstrate the utility of DASPfind by predicting 15 novel DTIs over a set of ion channel proteins, and confirming 12 out of these 15 DTIs through experimental evidence reported in literature and online drug databases. The second method (DASPfind+) modifies DASPfind in order to increase the confidence and reliability of the resultant predictions. Based on the structure of the drug-target interaction (DTI) networks, we introduced an optimization scheme that incrementally alters the network structure locally for each drug to achieve more robust top 1 ranked predictions. Moreover, we explored effects of several similarity measures between the targets on the prediction accuracy and proposed an enhanced strategy for DTI prediction. Our results show significant improvements of the accuracy of the top ranked DTI prediction over the current state-of-the-art methods.

The high cost and the long time required to bring drugs into commerce is driving efforts to repurpose FDA approved drugs—to find new uses for which they weren’t intended, and to thereby reduce the overall cost of commercialization, and shorten the lag between drug discovery and availability. In comparison to traditional drug repositioning, which relies on serendipitous clinical discoveries, computational methods can systemize the drug search and facilitate the drug development timeline even further. In this dissertation, I report on the development, testing and application of a promising new approach to drug repositioning. This novel computational drug repositioning method is based on mining a human functional linkage network for inversely correlated modules of drug and disease gene targets. Functional linkage network is an evidence-weighted network that provides a quantitative measure of the degree of functional association among any set of human genes. The method takes account of multiple information sources, including gene mutation, gene expression, and functional connectivity and proximity of within module genes. The method was used to identify candidates for treating breast and prostate cancer. We found that (i) the recall rate for FDA approved drugs for breast and (prostate) cancer is 20/20 (10/11), while the rates for drugs in clinical trials were 131/154 and (82/106); (ii) the Area Under the ROC Curve performance substantially exceeds that of two comparable previously published methods; (iii) preliminary in vitro studies indicate that 5/5 identified breast cancer candidates have therapeutic indices superior to that of Doxorubicin in Luminal-A (MCF7) and Triple-Negative (SUM149) breast cancer cell lines. I briefly discuss the biological plausibility of the candidates at a molecular level in the context of the biological processes that they mediate. In conclusion, our method provides a unique way of prioritizing disease causal genes and identifying drug candidates for repositioning, based on innovative computational method. The method appears to offer promise for the identification of multi-targeted drug candidates that can correct aberrant cellular functions. In particular the computational performance exceeded that of existing computational methods. The approach has the potential to provide a more efficient drug discovery pipeline.

Drug-induced gene expression profiles are rich information sources that can help to measure the effect of a drug on the transcriptional state of cells. However, the available experimental data only covers a limited set of conditions such as treatment time, dosages, and cell lines. This poses a challenge for neural network models to learn embeddings that can be generalized to new experimental conditions. In this project, we focus on the cell line as the confounder variable and train an Adversarial Neural Network to extract transcriptional effects that are conserved across multiple cell lines, and can thus be more confidently generalized to the biological setting of interest. Additionally, we investigate several methods to test whether our approach can simultaneously learn biologically valid embeddings and deconfound the effect of cell lines on the data distribution

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23565号 / 医博第4779号 / 新制||医||1054(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 武藤 学, 教授 万代 昌紀, 教授 上杉 志成 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(薬科学) / 甲第22752号 / 薬科博第126号 / 新制||薬科||14(附属図書館) / 京都大学大学院薬学研究科薬科学専攻 / (主査)教授 金子 周司, 教授 土居 雅夫, 教授 竹島 浩 / 学位規則第4条第1項該当 / Doctor of Pharmaceutical Sciences / Kyoto University / DFAM

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Systems biology is an interdisciplinary field that focuses on elucidating complex biological processes (systems) by investigating the interactions among its components through an iterative cycle composed of data generation, data analysis and mathematical modeling. Our contributions to systems biology revolve around the following two axes: - Data analysis: Two data analysis projects, which were initiated when I was a co-op at GlaxoSmithKline, are discussed in this thesis. First, next generation sequencing data generated for a phase I clinical trial is analyzed to determine the altered microbial community in human gut before and after antibiotic usage (Chapter 2). To our knowledge, there have not been similar comparative studies in humans on the impacts on the gut microbiome of an antibiotic when administered by different modes. Second, publicly available gene expression data is analyzed to investigate human immune response to tuberculosis (TB) infection (Chapter 3). The novel feature of this study is systematic drug repositioning for the prevention, control and treatment of TB using the Connectivity map. - Mathematical modeling: Polynomial dynamical systems, a state- and time- discrete logical modeling framework, is used to model two biological processes. First, a denitrification pathway in Pseudomonas aeruginosa is modeled to shed light on the reason of greenhouse gas nitrous oxide accumulation (Chapter 4). It is the first mathematical model of denitrification that can predict the effect of phosphate on the denitrification performance of this bacterium. Second, an iron homeostasis pathway linked to iron utilization, oxidative stress response and oncogenic pathways is constructed to investigate how normal breast cells become cancerous (Chapter 5). To date, our intracellular model is the only expanded core iron model that can capture a breast cancer phenotype by overexpression and knockout simulations. / Ph. D.

Les tumeurs gliales et glioneuronales sont les tumeurs cérébrales les plus fréquentes chez l'enfant. Elles sont généralement d'excellent pronostic. En revanche, les astrocytomes pilocytiques (AP) hypothalamo-chiasmatiques, ont un potentiel évolutif plus agressif. Ce travail de thèse propose une nouvelle stratégie thérapeutique pour ce sous-type d'AP selon la méthode du « drug repositioning », en employant la combinaison du celecoxib et de la fluvastatine. Nos travaux ont montré in vitro que cette association de molécules était synergique, capable d'arrêter le cycle cellulaire, de diminuer la prolifération et d'induire l'apoptose des cellules tumorales. Cette combinaison a également été testée avec succès chez une patiente souffrant d'un AP multifocal et réfractaire aux traitements conventionnels dans le cadre d'une thérapie métronomique. Ce manuscrit décrit également l'étude histo-moléculaire de plusieurs séries de tumeurs gliales et glioneuronales pédiatriques menées afin d'améliorer leur caractérisation et leur diagnostic. Nos travaux ont confirmé la présence de la fusion KIAA1549:BRAF dans les AP analysés ainsi que le caractère péjoratif de la topographie hypothalamo-chiasmatique, du variant histologique pilomyxoïde et de l'âge au diagnostic inférieur à 36 mois. Ils ont également montré l'absence de différence moléculaire entre les gliomes corticaux de grade II et des DNT. Enfin, nos travaux ont montré que les DNT, les GG et les PXA partagent la mutation BRAFV600E et l'expression de CD34. Ces travaux confirment l'implication majeure de l'altération de la voie des MAPKinases dans la tumorigenèse de ces tumeurs, constituant ainsi une cible thérapeutique prometteuse. / Glial and glioneuronal tumors are the most frequent brain tumors in children. They are characterized by an excellent prognosis. However, hypothalamic-chiasmatic pilocytic astrocytomas (PA) have a more aggressive outcome. In the first part, we propose a new therapeutic strategy for hypothalamic-chiasmatic PA according to drug repositioning method, by using celecoxib, and fluvastatin. We showed that, in vitro, this combination was synergistic, stopped cell cycle, inhibited cell proliferation and increased apoptosis. In addition, this combination was tested with success, under a metronomic chemotherapy, for a girl suffering from a multifocal PA and refractory to conventional treatment. This new strategy of treatment appears promising for this type of tumor because it is less toxic than conventional chemotherapy and not too expensive. In the second part, this manuscript describes the histo-molecular study of several retrospective series of glial and glioneuronal pediatric tumors conducted to improve their characterization and their diagnosis. We confirmed the presence of the fusion gene KIAA1549: BRAF in PA as well as the pejorative nature of the hypothalamic-chiasmatic topography, pilomyxoïde histology and the age at diagnosis less than 36 months. We also showed no molecular difference between cortical grade II gliomas associated with chronic epilepsy and the DNT group. Finally, we showed that DNT, GG and PXA share BRAFV600E mutation and expression of CD34. These studies confirm the major implication of the MAPKinase altered pathway in tumorigenesis of glial and glioneuronal pediatric tumors, constituting a promising therapeutic target.