The physiological process of cold acclimation protects plants native to the temperate regions of the world from the deleterious effects of low and freezing temperatures. This is achieved by a series of transcriptional, regulatory, and metabolic changes that enable continued growth and survival. Within minutes of exposure to temperatures below ca. 10°C, a complex cascade of transcriptional events is initiated to accomplish these changes. The initial alarm phase favors the rapid induction of a library of stress proteins with protective functions (e.g. COR proteins). This is followed by a cold hardened phase, characterized by maximal freezing tolerance, which continues until either the stress is removed, or the plant's metabolic and/or developmental state can no longer support maximal resistance. We have studied some of the important transcription factors and transcriptional changes associated with the initial alarm and later hardened phases of cold acclimation in the herbaceous annual Arabidopsis thaliana and the woody perennial Populus spp. We confirmed the functionality of the CBF-mediated signaling cascade in Poplar overexpressing AtCBF1, but noted that regulon composition and endogenous poplar CBF ortholog induction appeared to be tissue-specific. The lack of statistically significant DRE enrichment in the Poplar AtCBF1 regulons led us to investige cis-element abundance in the cold-associated transcription factor regulons of publicly available microarray data from Arabidopsis, leading to the development of a gene voting method of microarray analysis that we used to test for regulatory associations between transcription factors and their downstream cis-elements and gene targets. This analysis resulted in a new transcriptional model of the ICE1-mediated signaling cascade and implicated a role for phytochrome A. Application of this same method to microarray data from arabidopsis leaves developed at low temperature allowed us to identify a new cis-element, called DDT, which possessed enhancer-blocking function during the alarm stage of cold stress, but was enriched in the promoters of genes upregulated during the later cold hardened stages. As leaf growth and development at low temperature correlated with the enhancement freeze tolerance in Arabidopsis, we compared the transcriptomes of rapidly growing and fully grown poplar leaves at night (when both low temperatures and PhyA status might play important roles in nature), in the hopes of comparing this data with that of cold-treated leaves in the future. We identified the nocturnal mode of leaf growth in Populus deltoides as predominantly proliferative as opposed to expansive, and potentially linked to cellular carbohydrate status.

Metastasis is one of the critical hallmarks of malignancy tumor and the principal cause of death in patients with cancer. Cell migration is the basic and essential step in cancer metastasis process. To systematically investigate functional genes regulating cell migration and cancer metastasis on large scale, we developed a novel on-chip method, SAMcell (self-assembled cell microarray). This method was demonstrated to be particularly suitable for loss-of-function high-throughput screening because of its unique advantages. The first application of SAMcell was to screen human genome miRNAs, considering that more and more miRNAs had been proved to govern cancer metastasis. We found that over 20 % of miRNAs have migratory regulation activity in diverse cell types, indicating a general involvement of miRNAs in migratory regulation. Through triple-round screenings, we discovered miR-23b, which is down-regulated in human colon cancer samples, potently mediates the multiple steps of metastasis, including cell motility, cell growth and cell survival. In parallel, the second application of SAMcell was to screen human genome kinase genes, considering that more and more kinase genes had become successful diagnostic marker or drug targets. We found over 11% migratory kinase genes, suggesting the important role of kinase group in metastasis regulation. Through both functional screening and bioinformatics analysis, we discovered and validated 6 prospective metastasis-related kinase genes, which can be new potential targets in cancer therapy. These findings allow the understanding of regulation mechanism in human cancer progression, especially metastasis and provide the new insight into the biological and therapeutical importance of miRNAs or kinases in cancer.

Understanding variability of heath status is highly likely to be an important component of personalized medicine to predict health status of individuals and to promote personal health. Evidences of Genome Wide Association Study and gene expression study indicating that genetic factors affect the risk susceptibility of individuals have suggested adding genetic factors as a component of health status measurements. In order to validate or to predict health risk status with collected personal data such as clinical measurements or genomic data, it is important to have a well-established profile of diseases. The primary effort of this work was to find genomic evidence relevant to coronary artery disease. Two major methods of genomic analysis, gene expression profiling and GWAS on gene expression, were performed to dissect transcriptional and genotypic fingerprints of coronary artery disease. Blood-informative transcriptional Axes that can be described by 10 covariating transcripts per each Axis were utilized as a crucial measure of gene expression analysis. This study of the relationship between gene expression variation and various measurements of coronary artery disease delivered compelling results showing strong association between two transcriptional Axes and incident of myocardial infarction. 244 transcripts closely correlated with death by cardiovascular disease related events were also showing clear association with those two transcriptional Axes. These results suggest potential transcripts for use in risk prediction for the advent of myocardial infarction and cardiac death.

Pseudomonas aeruginosa is a metabolically versatile environmental bacterium that grows in extremely diverse habitats—from sea water to jet fuel—and is able to infect a large variety of organisms. It is a significant cause of human disease and is one of the most frequent healthcare-associated infections. P. aeruginosa uses a sophisticated gene regulatory network to adapt its growth strategy to these diverse environmental niches and the fluctuating conditions it encounters therein. The las and rhl “quorum sensing” (QS) intercellular communication systems play integral roles in this regulatory network and control the expression of factors important to the bacterium’s ecological fitness, including many secreted factors involved in nutrient acquisition, microbial competition, and virulence. These QS systems use diffusible acyl-homoserine lactone (AHL) signalling molecules to infer environmental parameters, including bacterial population density, and to coordinate behaviour across bacterial communities. This dissertation describes an investigation into the relationship between QS and small molecule primary metabolism, using metabolomic methods based on nuclear magnetic resonance spectroscopy and mass spectrometry. Analysis of extracellular metabolic profiles (the bacteria’s “metabolic footprint”) established that QS can modulate the uptake and excretion of primary metabolites and that this effect was strongest during the transition from exponential to stationary phase cell growth. Analysis of the cellular metabolome and proteome demonstrated that QS affected most major branches of primary metabolism, notably central carbon metabolism, amino acid metabolism and fatty acid metabolism. These data indicate that QS repressed acetogenesis and the oxidative C02-evolving portion of the TCA cycle, while inducing the glyoxylate bypass and arginine fermentation. QS also induced changes to fatty acid pools associated with lower membrane fluidity and higher chemical stability. Elevated levels of stress-associated polyamines were detected in QS mutants, which may be a consequence of a lack of QS-dependent adaptations. These findings suggest that wild-type QS directs metabolic adaptations to stationary phase stressors, including oxidative stress. Previous work, including several transcriptomic studies, has suggested that QS can play a role in primary metabolism. However, there has been no previous study of the global impact of AHL QS on the metabolome of P. aeruginosa. Research presented here demonstrates that QS induces a global readjustment in the primary metabolism and provides insight into QS- dependent metabolic changes during stationary-phase adaptation.

Made available in DSpace on 2014-06-11T19:26:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2009-11-06Bitstream added on 2014-06-13T20:54:13Z : No. of bitstreams: 1 ferreira_cb_me_jabo.pdf: 3799793 bytes, checksum: f1b3670130a5947eeee6d1e3151f9b69 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Fundecitrus / Dentro da comercialização de frutas, a citricultura é a mais importante. Representa para muitos países, dentre eles, os EUA e o Brasil, uma importante atividade econômica. Porém, esta atividade, vem sofrendo com inúmeras doenças e/ou pragas como a doença do Cancro Cítrico Asiático causada pela bactéria Xanthomonas citri subsp. citri (X. citri). Deste modo, com o objetivo do estudo do genoma funcional da X. citri, um banco de mutantes deste microorganismo foi obtido por meio de inserção aleatória do transposon Tn5, nas quais foram selecionados 53 mutantes que apresentaram, de forma superficial, alterações fenotípica em relação à X. citri selvagem. Para uma avaliação mais precisa, eles passaram por uma nova confirmação de suas alterações fenotípicas, onde foram inoculados em folhas de Citrus sinensis (Laranjeira pêra-Rio) e Citrus limonia (limoeiro cravo) e monitorados durante 16 dias, e aqueles que apresentaram as maiores alterações em relação à selvagem, tiveram confeccionadas para si curvas de crescimento in vivo. Conseguiu-se, desta forma, avaliar quantitativamente o processo patogênico, relacionar seus sintomas com dados numéricos e ainda descobrir detalhes até então não conhecidos. O mapeamento, dos respectivos loci mutados, foi realizado por seqüenciamento de DNA a partir do transposon, demonstrando que a metodologia empregada para a obtenção dos mutantes foi eficiente, conseguiu também revelar diversas proteína ainda hipotéticas, e outras, até então, não considerados como implicados no processo patogênico, como, uma Integrase, Fe-S oxidoredutase, Helicase IV, Receptor Dependente de Ton-B, entre outros / Concerning the commercialization of fruits, the citrus production is the most important. It represents for many countries, amongst them, U.S.A. and Brazil, an important economic activity. However, this activity has been suffering with many illnesses and/or plagues as the illness from the Asiatic citrus canker caused by the Xanthomonas citri subsp. citri bacterium (X. citri). In this way, from a bank of mutants of X. citri, gotten by means of random insertion of commercial one derived from transposon Tn5, had been selected 53 mutants that had presented, of superficial form, some type of phenotype alteration in relation to the wild X. citri. To a more necessary evaluation, each one of them passed for a new confirmation of its phenotype alterations, where they had been inoculated in leafs of Citrus sinensis ('Pera' sweet orange) and Citrus limonia ('Rangpur' lime) and monitored during 16 days, and those that had presented the biggest alterations in relation to the savage, had confectioned for itself in planta growth curves. We obtain, in such a way, to evaluate quantitatively the pathogenic process, to relate its symptoms with numerical data and still to discover not known details until then. The mapping of respective locus mutated was carried through by sequencing of DNA from transposon, demonstrating that the methodology used for the attainment of the mutants was efficient and still to disclose diverse genes still hypothetical, and others, until then, not considered as implied in the pathogenic process, as, Integrase, Fe-S oxidoredutase, Helicase IV, TonB-dependent receptor, among others

An important quest in genomics since the publication of the first complete human genome in 2003 has been its functional annotation. DNA holds the instructions to the production of the components necessary for the life of cells and organisms. A complete functional catalog of genomic regions will help the understanding of the cell body and its dynamics, thus creating links between genotype and phenotypic traits. The need for annotations prompted the development of several bioinformatic methods. In the context of promoter and first exon predictors, the majority of models relies principally on structural and chemical properties of the DNA sequence. Some of them integrate information from epigenomic and transcriptomic data as secondary features. Current genomic research asserts that reference genome annotations are far from being fully annotated (human organism included).Physicians rely on reference genome annotations and functional databases to understand disorders with genetic basis, and missing annotations may lead to unresolved cases. Because of their complexity, neurodevelopmental disorders are under study to figure out all genetic regions that are involved. Besides functional validation on model organisms, the search for genotype-phenotype association is supported by statistical analysis, which is typically biased towards known functional regions.This thesis addresses the use of an in-silico integrative analysis to improve reference genome annotations and discover novel functional regions associated with neurodevelopemental disorders. The contributions outlined in this document have practical applications in clinical settings. The presented bioinformatic method is based on epigenomic and transcriptomic data, thus excluding features from DNA sequence. Such integrative approach applied on brain data allowed the discovery of two novel promoters and coding first exons in the human DLG2 gene, which were also found to be statistically associated with neurodevelopmental disorders and intellectual disability in particular. The application of the same methodology to the whole genome resulted in the discovery of other novel exons expressed in brain. Concerning the in-silico method itself, the research demanded a high number of functional and clinical datasets to properly support and validate our discoveries.This work describes a bioinformatic method for genome annotation, in the specific area of promoter and first exons. So far the method has been applied on brain data, and the extension to the whole body data would be a logical by-product. We will leverage distributed frameworks to tackle the even higher amount of data to analyse, a task that has already begun. Another interesting research direction that came up from this work is the temporal enrichment analysis of epigenomics data across different developmental stages, in which changes of epigenomic enrichment suggest time-specific and tissue-specific functional gene and gene isoforms regulation. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Os micoplasmas são os menores microrganismos capazes de autoreplicação conhecidos na natureza, responsáveis por uma série de doenças no homem e nos animais, infectando ainda plantas e insetos. Constituem um grande grupo de bactérias, ordenadas em diferentes gêneros na classe Mollicutes, cuja principal característica em comum, além do genoma reduzido, é a ausência de parede celular. Mycoplasma mycoïdes subsp. mycoïdes SC, responsável pela Pleuropneumonia Contagiosa Bovina, foi o primeiro microrganismo desta classe de bactérias a ser identificado. Esta é uma doença bastante grave, com altas taxas de morbidade e mortalidade. A variedade Mycoplasma mycoïdes subsp. mycoïdes LC é responsável principalmente por casos de Pleuropneumonia Contagiosa Caprina, mastite no gado bovino, e ainda artrite em ovinos e caprinos em menor extensão. M. capricolum é um patógeno caprino, responsável principalmente por casos de artrite com grande importância econômica na medicina veterinária. M. pulmonis é um patógeno de roedores, considerado como o melhor modelo experimental para o estudo das micoplasmoses respiratórias. M. genitalium, o menor microrganismo conhecido capaz de se autoreplicar, é um patógeno humano responsável por casos de uretrite não gonocócica, cujo seqüenciamento completo do cromossomo tornou-se um marco na era da genômica. O estudo funcional do genoma destes micoplasmas, para a compreensão de sua biologia e patogenicidade, requer o desenvolvimento de ferramentas genéticas eficientes. No presente trabalho, análises in silico das seqüências na região das prováveis origens de replicação cromossômica (oriC) destes micoplasmas demonstraram a existência de possíveis DnaA boxes localizados em torno do gene dnaA. Estas regiões oriC foram caracterizadas funcionalmente após sua clonagem em vetores artificiais e a transformação dos micoplasmas com os plasmídeos recombinantes resultantes. O plasmídeo pMPO1, contendo a região oriC de M. pulmonis, sofreu integração no cromossomo do micoplasma por recombinação homóloga após poucas passagens in vitro. A redução desta oriC para o fragmento contendo somente os DnaA boxes localizados nas estremidades 5´ou 3´do gene dnaA não foi capaz de produzir plasmídeos replicativos em M. pulmonis, exceto quando estes dois fragmentos foram clonados no mesmo vetor, espaçados pelo determinante de resistência à tetraciclina tetM. Um fragmento interno do gene da hemolisina A (hlyA) de M. pulmonis foi clonado nestes plasmídeos oriC, e os vetores resultantes foram utilizados para transformar o micoplasma. A integração destes vetores por um crossing-over com o gene hlyA, causando a sua dirupção, foi documentada. Deste modo, estes plasmídeos oriC podem vir a se tornar ferramentas genéticas valiosas para o estudo do papel de genes específicos, notadamente aqueles potencialmente envolvidos na patogênese. / Mycoplasmas are the smallest microorganisms capable of self replication known to date, responsible for many diseases in man and animals, infecting also plants and insects. They constitute a large group of bacteria, classified in different genera in the class Mollicutes, which main common characteristic, besides the small genome, is the absence of a cell wall. Mycoplasma mycoïdes subsp. mycoïdes SC, responsible for the Bovine Contagious Pleuropneumonia, was the first microorganism of this class of bacteria to be identified. That is a quite severe disease, with high morbidity and mortality rates. Mycoplasma mycoïdes subsp. mycoïdes LC is responsible mainly for cases of Caprine Contagious Pleuropneumonia, mastitis in cattle, and also arthritis in goats and sheep in less extension. M. capricolum is a pathogen of goats, responsible mainly by cases of arthritis with large economic impact in veterinary medicine. M. pulmonis is a rodent pathogen, considered to be the best experimental model for studying respiratory mycoplasmoses. M. genitalium, the smallest microorganism capable of self replication, is an human pathogen responsible for cases of non gonococcal urethritis, which complete chromosome sequencing has become a benchmark in the era of genomics. Functional studies of these mycoplasma genomes, for comprehension of their biology and pathogenicity, requires the development of efficient genetic tools. In the present work, in silico analysis of sequences of the putative origin of chromosome replication (oriC) region of these mycoplasmas demonstrates the existence of putative DnaA boxes located around the dnaA gene. These oriC regions were functionally characterized after cloning into artificial vectors and transformation of mycoplasmas with the resulting recombinant plasmids. The plasmid pMPO1, which contains the M. pulmonis oriC region, has integrated into the mycoplasma chromosome by homologous recombination after a few in vitro passages. Reduction of this oriC to the fragment containing only the DnaA boxes located upstream or downstream the dnaA gene could not produce plasmids able to replicate in M. pulmonis, except when these two fragments were cloned in the same vector, spaced by tetracycline resistance gene tetM. An internal fragment of the M. pulmonis hemolysine A gene (hlyA) was cloned into these oriC plasmids, and the resulting vectors were used to transform the mycoplasma. Integration of these disruption vectors by one crossing-over with the hlyA gene could be documented. Therefore, these oriC plasmids may become valuable genetic tools for studying the role of specific genes of mycoplasmas, specially those potentially involved in pathogenesis.

[ES] La comprensión de los aspectos de la funcionalidad de las células que cuentan para los mecanismos de las enfermedades es el mayor reto de la medicina personalizada. A pesar de la disponibilidad creciente de los datos de genómica y transcriptómica, sigue existiendo una notable brecha entre la detección de las perturbaciones en la expresión de genes y la comprensión de su contribución en los mecanismos moleculares que últimamente tienen relación importante con el fenotipo estudiado. A lo largo de la última década, distintos modelos computacionales y matemáticos se han propuesto para el análisis de las rutas. Sin embargo, estos modelos no toman en cuenta los mecanismos dinámicos de las rutas como la estructura y las interacciones entre genes y proteínas. En esta tesis doctoral, presento dos modelos matemáticos ligeramente distintos, para integrar los datos transcriptómicos masivos de humano con un conocimiento previo de de las rutas de señalización y metabólicas para estimar las actividades mecánicas que están detrás de esas rutas (MPAs). Las MPAs son variables continuas con valores de nivel individual que pueden ser usadas con los modelos de aprendizaje de máquinas y métodos estadísticos para determinar los biomarcadores que podemos usar para los diagnósticos tempranos y la clasificación de subtipos de enfermedades, además de poder sugerir las dianas terapéuticas potenciales para las intervenciones individualizadas. El objetivo global es desarrollar nuevos y avanzados enfoques de la biología de sistemas para proponer unas hipótesis funcionales que nos ayuden a entender e interpretar los mecanismos complejos de las enfermedades. Estos mecanismos son cruciales para mejorar los tratamientos personalizados y predecir los resultados clínicos. En primer lugar, contribuí al desarrollo de un método que está diseñado para extraer las subrutas elementales desde la ruta de señalización con sus actividades estimadas. Posteriormente, este algoritmo se ha adaptado a los módulos metabólicos y se ha implementado como una herramienta web. Finalmente , el método ha revelado un panorama metabólico para una lista completa de diferentes tipos de cánceres. En este estudio, analicé el perfil metabólico de 25 tipos de cáncer distintos y se validó el método usando varios enfoques computacionales y experimentales. Cada método desarrollado en esta tesis ha sido enfrentado a otros métodos similares existentes, evaluados por sus sensibilidades y especificidades, experimentalmente validados cuando fue posible y usados para predecir resultados clínicos de varios tipos de cánceres. La investigación descrita en esta tesis y los resultados obtenidos fueron publicados en distintas revistas arbitradas que están relacionadas con el cáncer y biología de sistemas, y también en los periódicos nacionales. / [CA] La comprensió dels aspectes de la funcionalitat de les cèl·lules que compten per als mecanismes de les malalties és el major repte de la medicina personalitzada. Malgrat la disponibilitat creixent de les dades de genòmica i transcriptómica, continua existint una notable bretxa entre la detecció de les pertorbacions en l'expressió de gens i la comprensió de la seua contribució en els mecanismes moleculars que últimament tenen relació important amb el fenotip estudiat. Al llarg de l'última dècada, diferents models computacionals i matemàtics s'han proposat per a l'anàlisi de les rutes. No obstant això, aquests models no tenen en compte els mecanismes dinàmics de les rutes com l'estructura i les interaccions entre gens i proteïnes. En aquesta tesi doctoral, presente dos models matemàtics lleugerament diferents, per a integrar les dades transcriptómicos massius d'humà amb un coneixement previ de de les rutes de senyalització i metabòliques per a estimar les activitats mecàniques que estan darrere d'aqueixes rutes (MPAs). Les MPAs són variables contínues amb valors de nivell individual que poden ser usades amb els models d'aprenentatge de màquines i mètodes estadístics per a determinar els biomarcadores que podem usar per als diagnòstics primerencs i la classificació de subtipus de malalties, a més de poder suggerir les dianes terapèutiques potencials per a les intervencions individualitzades. L'objectiu global és desenvolupar nous i avançats enfocaments de la biologia de sistemes per a proposar unes hipòtesis funcionals que ens ajuden a entendre i interpretar els mecanismes complexos de les malalties. Aquests mecanismes són crucials per a millorar els tractaments personalitzats i predir els resultats clínics. En primer lloc, vaig contribuir al desenvolupament d'un mètode que està dissenyat per a extraure les subrutas elementals des de la ruta de senyalització amb les seues activitats estimades. Posteriorment, aquest algorisme s'ha adaptat als mòduls metabòlics i s'ha implementat com una eina web. Finalment, el mètode ha revelat un panorama metabòlic per a una llista completa de diferents tipus de càncers. En aquest estudi, vaig analitzar el perfil metabòlic de 25 tipus de càncer diferents i es va validar el mètode usant diversos enfocaments computacionals i experimentals. Cada mètode desenvolupat en aquesta tesi ha sigut enfrontat a altres mètodes similars existents, avaluats per les seues sensibilitats i especificitats, experimentalment validats quan va ser possible i usats per a predir resultats clínics de diversos tipus de càncers. La investigació descrita en aquesta tesi i els resultats obtinguts van ser publicats en diferents revistes arbitrades que estan relacionades amb el càncer i biologia de sistemes, i també en els periòdics nacionals. / [EN] Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is the main challenge for precision medicine. In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Over the last decade, different computational and mathematical models have been proposed for pathway analysis. However, they are not taking into account the dynamic mechanisms contained by pathways as represented in their layout and the interactions between genes and proteins. In this thesis, I present two slightly different mathematical models to integrate human transcriptomic data with prior knowledge of signalling and metabolic pathways to estimate the Mechanistic Pathway Activities (MPAs). MPAs are continuous and individual level values that can be used with machine learning and statistical methods to determine biomarkers for the early diagnosis and subtype classification of the diseases, and also to suggest potential therapeutic targets for individualized therapeutic interventions. The overall objective is, developing new and advanced systems biology approaches to propose functional hypotheses that help us to understand and interpret the complex mechanism of the diseases. These mechanisms are crucial for robust personalized drug treatments and predict clinical outcomes. First, I contributed to the development of a method which is designed to extract elementary sub-pathways from a signalling pathway and to estimate their activity. Second, this algorithm adapted to metabolic modules and it is implemented as a webtool. Third, the method used to reveal a pan-cancer metabolic landscape. In this study, I analyzed the metabolic module profile of 25 different cancer types and the method is also validated using different computational and experimental approaches. Each method developed in this thesis was benchmarked against the existing similar methods, evaluated for their sensitivity and specificity, experimentally validated when it is possible and used to predict clinical outcomes of different cancer types. The research described in this thesis and the results obtained were published in different systems biology and cancer-related peer-reviewed journals and also in national newspapers. / Cubuk, C. (2020). Modeling Functional Modules Using Statistical and Machine Learning Methods [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/156175 / TESIS

Endocytosis is an essential cellular process consisting of the internalization of extracellular cargo and its transport towards different intracellular destinations. Multiple endocytic routes are tailored for the internalization and trafficking of different types of cargo and multiple endocytic organelles provide specialized biochemical environments where different molecular events take place. Membrane receptors and cargo molecules are internalized by both Clathrin-dependent and –independent endocytosis into early endosomes. From here two main endocytic routes are followed: 1) the recycling route, mainly followed by membrane receptor and other molecules like Transferrin, brings the cargo back to the plasma membrane and 2) the degradative route, followed by molecules like Epidermal Growth Factor (EGF) and Lipoprotein particles (LDL), leads the cargo to degradation into late endosomes/lysosomes. In addition to the basic function of intracellular cargo transport, the endocytic system fulfils many other cellular and developmental functions such as transmission of proliferative and survival signals and defence against pathogens. In order for cells to properly perform their various and numerous functions in organs and tissues, the activity of the endocytic system needs to be coordinated between cells and, within individual cells, integrated with other cellular functions. Even though molecules orchestrating the endocytic sorting and transport of different types of cargo have long been investigated, our understanding of the molecular machinery underlying endocytosis and its coordination into the cellular systems remains fragmentary. The work presented in this thesis aimed at understanding how this high-order regulation and integration is achieved. This requires not only a comprehensive analysis of molecular constituents of the endocytic system but also an understanding of the general design principles underlying its function. To this end, in collaboration with several members of the Zerial group and with the HT-Technology Development Studio (TDS) at MPI-CBG, I developed a new strategy to accurately profile the activity of human genes with respect to Transferrin (Tfn) and Epidermal Growth Factor (EGF) endocytosis by combining genome-wide RNAi with several siRNA/esiRNA per gene, automated high-resolution confocal microscopy, quantitative multi-parametric image analysis and high-performance computing. This provided a rich and complex genomic dataset that was subsequently subjected to analysis with a combination of tools such as a multi-parametric correlation of oligo profiles, phenotypic clustering and pathways analysis, and a Bayesian network reconstruction of key endocytic features. Altogether, the genomic endeavour and the subsequent analyses provided a number of important results: first, they revealed a much higher extent of off-target effects from RNAi and provided novel tools to infer the specific effects of genes loss of function; second, they identified a large number of novel molecules exerting a regulatory role on the endocytic system, including uncharacterized genes and genes implicated in human diseases; third, they uncovered the regulatory activity of signalling pathways such as Wnt, Integrin, TGF-β, and Notch, and found new genes regulating the sorting of cargo to a specialized subset of early endosomes that function as intracellular signalling platforms; and fourth, a systems analysis by Bayesian networks revealed that the cell specifically regulates the number, size, concentration of cargo and intracellular position of endosomes, thus uncovering novel properties of the endocytic system. In conclusion, the work presented here not only provided a dataset extremely rich of information whose potential has just begun to be uncovered but also shows how genomic datasets can be used to reveal design principles governing the functioning of biological processes.

No description available.