Regulatory mechanisms of mammalian replication origins

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 14-12-2017Esta tesis tiene embargado el acceso al texto completo hasta el 14-06-2019In mammalian cells, DNA replication starts from thousands of replication origins whose activation is tightly regulated in the cell division cycle. In this study, we have aimed at better understanding the regulation of origin selection and activation in mouse embryonic stem cells and human cancer cells. In the first part of this dissertation, we set out to investigate the dynamics of origin activation in response to stress conditions that slow down or stall replication forks. This situation promotes the activation of otherwise ‘dormant’ origins, which provide a backup mechanism to complete replication and prevent genomic instability. Using the SNS-Seq technique based on deep-sequencing of short nascent DNA strands, we have mapped the genomic positions of origins in control growth conditions and two experimental settings that trigger the activation of extra origins: (a) exposure to DNA polymerase inhibitor aphidicolin; (b) overexpression of CDC6, a limiting factor for origin licensing and activation in primary murine cells. Using SNS-Seq data we also determined the efficiency of activation of each individual origin in the cell population. Constitutive origins, in contrast to stress-responsive ones, show a strong preference towards open, transcriptionally active chromatin and display higher efficiency. Our results strongly suggest that the main response to stress is mediated by modulating the activity of pre-existing origins rather than the activation of new ones. We have also carried out an unprecedented integration of linear origin maps into 3D chromatin networks that reveals how origins tend to group together in clusters that likely correspond to DNA replication factories. Origin connections are found within the same topologically associated domain (TAD), but also between origins located in different TADs. We report for the first time that the connectivity of an origin is directly proportional to its efficiency of activation. In the second part, we aimed at dissecting a novel mechanism that regulates CDC6 protein stability. Downregulation of CDC7 kinase, known to activate the MCM helicase at replication origins, caused a drop in cellular CDC6 levels. CDC6 was phosphorylated by CDC7 in vitro and became destabilized in vivo when all possible CDC7-dependent phosphorylation sites were mutated. We report a previously unknown role of CDC7 in the regulation of CDC6 stability that is mediated by a combination of direct and indirect mechanisms.This Thesis was supported by a “La Caixa”/CNIO Fellowship from the International PhD Programme and research grants from the Spanish Ministerio de Economía y Competitividad (BFU2013-49153-P and BFU2016-80402-R

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures

Higher-order dynamics on complex networks

L’estudi de les xarxes complexes ha esdevingut un nou paradigma a l’hora d’entendre i modelar sistemes físics. Uns dels principals punts d’interès són les dinàmiques que hi podem modelar. Però com en tot model, la quantitat de informació que podem representar-hi està limitada per la seva complexitat. La motivació principal d’aquesta tesi és l’estudi de l’efecte que un increment de la complexitat estructural, relacional i temporal té sobre tres importants àrees d’estudi: l’evolució de la cooperació, la propagació de malalties, i l’estudi de la mobilitat humana. En aquest treball hem utilitzat dilemes socials per estudiar com evoluciona la cooperació dins d’una població. Incrementant l’ordre de complexitat estructural de les xarxes, permetent que els individus és puguin relacionar en diferents contextos socials, s’ha mostrat cabdal a l’hora d’explicar algunes característiques sobre l’aparició de comportaments altruistes. Utilitzant aquestes noves estructures, les xarxes multicapa, permetem als membres de la població cooperar en determinat contextos i de no fer-ho en d’altres i això, com analíticament demostrem, augmenta l’espectre d’escenaris allà on cooperació i defecció poden sobreviure. Seguidament, estudiem els models de propagació de malalties des de el punt de vista dels enllaços entre individus. Amb aquest augment de la complexitat relacional dels models epidèmics, aconseguim extreure informació que ens permet, entre altres coses, definir una mesura d’influència d’un enllaç a la propagació de l’epidèmia. Utilitzem aquest fet per a proposar una nova mesura de contenció, basada en l’eliminació dels enllaços més influents, que es mostra més eficient que altres mètodes previs. Finalment, proposem un mètode per a descriure la mobilitat que permet capturar patrons recurrents i heterogeneïtats en els temps que els individus estan en un lloc abans de desplaçar-se a un altre. Aquestes propietats són intrínseques a la mobilitat humana i el fet de poder-les capturar, tot i el cost d’augmentar l’ordre temporal, és crític, com demostrem, a l’hora de modelar com les epidèmies és difonen per mitja del moviment de les persones.El estudio de redes complejas se ha convertido en un nuevo paradigma para comprender y modelar sistemas físicos. Uno de los principales puntos de interés son las dinámicas que podemos modelar. Pero como en todo modelo, la cantidad de información que podemos representar está limitada por su complejidad. La motivación principal de esta tesis es estudiar el efecto que un incremento de la complejidad estructural, relacional y temporal tiene sobre tres importantes áreas de estudio: la evolución de la cooperación, la propagación de enfermedades, y el estudio de la movilidad humana. En este trabajo hemos utilizado dilemas sociales para estudiar cómo evoluciona la cooperación dentro de una población. Incrementando el orden de complejidad estructural de las redes, permitiendo que los individuos se puedan relacionar en diferentes contextos sociales, se ha demostrado capital para explicar algunas de las características sobre la aparición de comportamientos altruistas. Utilizando estas nuevas estructuras, las redes multicapa, permitimos a los miembros de la población cooperar en determinados contextos y no hacerlo en otros, con lo que, como demostramos analíticamente, aumenta el espectro de escenarios en los que la cooperación y la defección pueden sobrevivir. A continuación, estudiamos modelos de propagación de enfermedades desde el punto de vista de los enlaces entre individuos. Con este aumento de complejidad relacional de los modelos epidémicos, conseguimos extraer información que nos permite, entre otras cosas, definir una medida de contención, basada en la eliminación de los enlaces más influyentes, que se muestra más eficaz que otros métodos previos. Finalmente, proponemos un método para describir la movilidad que permite capturar patrones recurrentes y heterogeneidades en los tiempos que los individuos están en un lugar antes de desplazarse a otro. Estas propiedades son intrínsecas a la movilidad humana y el hecho de poder capturarlas, a pesar de incrementar el orden temporal, es crítico, como demostramos, para modelar cómo las epidemias se difunden por medio del movimiento de las personas.The study of complex networks has become a new paradigm to understand and model physical systems. One of the points of interest is the dynamics that we can model. However, as with any model, the amount of information that we can represent is limited by its complexity. The primary motivation of this thesis is the study of the effect that an increase in structural, relational and temporal complexity has on three critical areas of study: the evolution of cooperation, epidemic spreading and human mobility. In this work, we have used social dilemmas to study how cooperation within a population evolves. Increasing the order of structural complexity of the networks, allowing individuals to interact in different social contexts, has shown to be crucial to explain some features about the emergence of altruistic behaviors. Using these new structures, multilayer networks, we allow members of the population to cooperate in specific contexts and defect in others, and this, as we analytically demonstrate, increases the spectrum of scenarios where both strategies can survive. Next, we study the models of epidemic spreading from the point of view of the links between individuals. With this increase in the relational complexity of the epidemic models, we can extract information that allows us, among other things, to define a measure of the contribution of a link to the spreading. We use this metric to propose a new containment measure, based on the elimination of the most influential links, which is more effective than other previous methods. Finally, we propose a method to describe mobility that allows capturing recurrent and heterogeneous patterns in the times that individuals stay in a place before moving to another. These properties are intrinsic to human mobility, and the fact of being able to capture them, despite the cost of increasing the temporal order is critical, as we demonstrate, when it comes to modeling how epidemics spread through the movement of the people

Adaptive networks for robotics and the emergence of reward anticipatory circuits

Currently the central challenge facing evolutionary robotics is to determine how best to extend the range and complexity of behaviour supported by evolved neural systems. Implicit in the work described in this thesis is the idea that this might best be achieved through devising neural circuits (tractable to evolutionary exploration) that exhibit complementary functional characteristics. We concentrate on two problem domains; locomotion and sequence learning. For locomotion we compare the use of GasNets and other adaptive networks. For sequence learning we introduce a novel connectionist model inspired by the role of dopamine in the basal ganglia (commonly interpreted as a form of reinforcement learning). This connectionist approach relies upon a new neuron model inspired by notions of energy efficient signalling. Two reward adaptive circuit variants were investigated. These were applied respectively to two learning problems; where action sequences are required to take place in a strict order, and secondly, where action sequences are robust to intermediate arbitrary states. We conclude the thesis by proposing a formal model of functional integration, encompassing locomotion and sequence learning, extending ideas proposed by W. Ross Ashby. A general model of the adaptive replicator is presented, incoporating subsystems that are tuned to continuous variation and discrete or conditional events. Comparisons are made with Ross W. Ashby's model of ultrastability and his ideas on adaptive behaviour. This model is intended to support our assertion that, GasNets (and similar networks) and reward adaptive circuits of the type presented here, are intrinsically complementary. In conclusion we present some ideas on how the co-evolution of GasNet and reward adaptive circuits might lead us to significant improvements in the synthesis of agents capable of exhibiting complex adaptive behaviour

Knowledge derivation and data mining strategies for probabilistic functional integrated networks

PhDOne of the fundamental goals of systems biology is the experimental verification of the interactome: the entire complement of molecular interactions occurring in the cell. Vast amounts of high-throughput data have been produced to aid this effort. However these data are incomplete and contain high levels of both false positives and false negatives. In order to combat these limitations in data quality, computational techniques have been developed to evaluate the datasets and integrate them in a systematic fashion using graph theory. The result is an integrated network which can be analysed using a variety of network analysis techniques to draw new inferences about biological questions and to guide laboratory experiments. Individual research groups are interested in specific biological problems and, consequently, network analyses are normally performed with regard to a specific question. However, the majority of existing data integration techniques are global and do not focus on specific areas of biology. Currently this issue is addressed by using known annotation data (such as that from the Gene Ontology) to produce process-specific subnetworks. However, this approach discards useful information and is of limited use in poorly annotated areas of the interactome. Therefore, there is a need for network integration techniques that produce process-specific networks without loss of data. The work described here addresses this requirement by extending one of the most powerful integration techniques, probabilistic functional integrated networks (PFINs), to incorporate a concept of biological relevance. Initially, the available functional data for the baker’s yeast Saccharomyces cerevisiae was evaluated to identify areas of bias and specificity which could be exploited during network integration. This information was used to develop an integration technique which emphasises interactions relevant to specific biological questions, using yeast ageing as an exemplar. The integration method improves performance during network-based protein functional prediction in relation to this process. Further, the process-relevant networks complement classical network integration techniques and significantly improve network analysis in a wide range of biological processes. The method developed has been used to produce novel predictions for 505 Gene Ontology biological processes. Of these predictions 41,610 are consistent with existing computational annotations, and 906 are consistent with known expert-curated annotations. The approach significantly reduces the hypothesis space for experimental validation of genes hypothesised to be involved in the oxidative stress response. Therefore, incorporation of biological relevance into network integration can significantly improve network analysis with regard to individual biological questions

Analysis of large-scale metabolic networks: organization theory, phenotype prediction and elementary flux patterns

Analyzing metabolic networks is one of the central topics in systems biology. Thus, different methods for analyzing the structure of such networks have been proposed. The methods used here can be broadly divided into two types: flux-centered approaches like elementary mode analysis (EM analysis), the closely related extreme pathway analysis as well as flux balance analysis (FBA) and approaches like chemical organization theory (OT), that additionally explicitly takes into account metabolites. The aim of this work is the integration of these concepts in order to allow for a more comprehensive analysis of metabolic networks. Indeed, EM analysis and OT can complement each other in two ways. First, the set of chemical organizations of a reaction network allows for a clustering of EMs and helps to identify those EMs that cannot operate at steady state. Second, the set of EMs can be used to compute chemical organizations. In another direction, the combination FBA and EM analysis helps to overcome the problem that the entire set of EMs cannot be computed in large metabolic networks. The framework behind this integration, elementary flux pattern analysis (EFP analysis), allows one to identify all possible routes through a subsystem that are part of a pathway in a genome-scale metabolic network. An important benefit of this concept is that it allows to apply many approaches building on EM analysis to genome-scale metabolic networks. Furthermore, using EFP analysis, we identified several EMs in a subsystem of a metabolic model of Escherichia coli that are not compatible with a pathway on the genome scale. Additionally, we discovered several alternative routes to metabolic pathways in the central metabolism of E. coli. Using EFP analysis in a genome-scale metabolic model of humans we found several pathways that contradict the widely held assumption in biochemistry that the conversion of even-chain fatty acids into glucose is infeasible in humans

Comparative genomics and systems biology of environmental stress responses relevant to fungal virulence

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