計数に基づく生体分子ネットワークの特性評価における計算論および化学論的困難

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 陶山 明, 東京大学准教授 新井 宗仁, 東京大学准教授 吉本 敬太郎, 東京大学講師 河村 彰星, 東京大学准教授 渋谷 哲朗University of Tokyo(東京大学

The Metabolic Core and Catalytic Switches Are Fundamental Elements in the Self-Regulation of the Systemic Metabolic Structure of Cells

[Background] Experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a metabolic core formed by a set of enzymatic reactions which are always active under all environmental conditions, while the rest of catalytic processes are only intermittently active. The reactions of the metabolic core are essential for biomass formation and to assure optimal metabolic performance. The on-off catalytic reactions and the metabolic core are essential elements of a Systemic Metabolic Structure which seems to be a key feature common to all cellular organisms. [Methodology/Principal Findings] In order to investigate the functional importance of the metabolic core we have studied different catalytic patterns of a dissipative metabolic network under different external conditions. The emerging biochemical data have been analysed using information-based dynamic tools, such as Pearson's correlation and Transfer Entropy (which measures effective functionality). Our results show that a functional structure of effective connectivity emerges which is dynamical and characterized by significant variations of bio-molecular information flows. [Conclusions/Significance] We have quantified essential aspects of the metabolic core functionality. The always active enzymatic reactions form a hub –with a high degree of effective connectivity- exhibiting a wide range of functional information values being able to act either as a source or as a sink of bio-molecular causal interactions. Likewise, we have found that the metabolic core is an essential part of an emergent functional structure characterized by catalytic modules and metabolic switches which allow critical transitions in enzymatic activity. Both, the metabolic core and the catalytic switches in which also intermittently-active enzymes are involved seem to be fundamental elements in the self-regulation of the Systemic Metabolic Structure.Consejo Superior de Investigaciones Cientificas (CSIC),grant 201020I026. Ministerio de Ciencia e Innovacion (MICINN). Programa Ramon y Cajal. Campus de Excelencia Internacional CEI BioTIC GENIL, grant PYR-2010-14. Junta de Andalucia, grant P09-FQM-4682

SMAD4: a multifunctional regulator of limb bud initiation and outgrowth

During mouse embryonic development, the spatio-temporal expression of genes is controlled by both interlinked signalling pathways and interactions between transcription factors and their target cis-regulatory modules. To gain global insights into the roles of a trans-acting transcriptional regulator in a specific tissue, the genome-wide profiling of its target regulatory regions and their association with the putative target genes are essential. Therefore, I have combined several types of genome-wide analyses such as ChIP-seq using epitope-tagged transcription factors with ATAC-seq and RNA-seq to study the functions of HAND2 and SMAD4 during heart and limb bud development, respectively. In Hand2-deficient embryos, we observed that cells of the atrioventricular canal do not undergo the endothelial-mesenchymal transition that underlies cardiac cushion development. By combining HAND23xF ChIP-seq and RNA-seq analysis, we have identified the HAND2 gene regulatory network involved in these processes and show that HAND2 is a key regulator of heart valve development. Limb bud outgrowth and patterning are regulated by a self-regulatory feedback signalling system operating between the SHH and FGF signalling pathways that critically depends on the BMP antagonist GREMLIN1. However, the establishment of these signalling feedback loops requires initiation of Gremlin1 expression by high BMP activity. For my PhD research, I have investigated the roles of the BMP signalling pathway during limb bud initiation by studying the functions of the BMP signal transducer SMAD4. By combining genome-wide SMAD43xF ChIP-seq, ATAC-seq and RNA-seq analyses, I am able to show that SMAD4 participates in activation of Gremlin1 expression by interacting with Grem1 coding exon 2 (a putative regulatory region). Furthermore, the identification of the SMAD4 gene regulatory network reveals multiple functions of SMAD4 during the onset of limb bud development. Especially, SMAD4 directly regulates target genes involved in limb bud outgrowth and patterning. Rather unexpected, my analysis reveals that SMAD4 directly regulates cholesterol homeostasis and controls the gradient and activity of the SHH signalling pathway during early limb bud development

Develoment and Application of Chemical Strategies to Study Protein Fatty-Acylation in Eukaryotes

Reversible S-palmitoylation confers spatiotemporal control of protein function by modulating protein stability, trafficking and activity as well as protein-protein and membrane-protein associations. While it is evident that palmitoylation is regulated in vivo, mechanisms that mediate cellular stimuli-driven changes of the lipid modification are not understood. Furthermore, the requirement for substrate specificity among the highly redundant palmitoyl acyltransferases (PATs) remains unresolved. To study the regulation of PATs and palmitoylomes, I developed bioorthogonal chemical strategies for improved analysis of dynamic palmitoylation in mammalian cells. I showed that alkyne-functionalized fatty acids, in conjunction with azido-fluorophores, provide the most sensitive detection of acylated proteins following CuI-catalyzed azidealkyne cycloaddition. Linkage-specific hydrolysis, mutagenesis and inhibitor studies reveal that these alkynyl-fatty acids are incorporated into proteins by endogenous fattyacylation machinery via native linkages at specific amino acid residues. In addition, shorter and longer chain fatty acids label myristoylated and palmitoylated proteins respectively. Since myristoylation is co-translational and constitutive, I employed both palmitoylation and myristoylation chemical reporters with orthogonal fluorophores to simultaneously monitor palmitate and protein turnover. Dual pulse-chase analysis of Lck, a tyrosine kinase required for T-cell signaling, revealed accelerated palmitate cycling upon T-cell activation. Pharmacological perturbation of Lck palmitate turnover suggests yet uncharacterized serine hydrolases contribute to dynamic palmitoylation in cells. These significant improvements allow rapid and robust biochemical analysis of palmitoylated proteins without overexpression, facilitating the functional characterization of cellular factors and drugs that modulate protein palmitoylation. Taking advantage of the sensitive bioorthogonal detection of protein palmitoylation and the simple PAT network in the fission yeast Schizosaccharomyces pombe, I provided evidence for regulation of PATs and palmitoylomes in vivo at physiological enzyme and substrate concentrations. I showed that the Erf2-Erf4 PAT modulates sexual differentiation, and that upregulation of its expression is required to establish the meiotic palmitoylome. Importantly, I demonstrated that changes in Erf2- Erf4 levels within the physiological range control PAT specificity and result in the differential palmitoylation of its substrates in vegetative and meiotic cells. Underscoring the biological significance of controlling PAT levels, Erf2-Erf4 overproduction in proliferating cells alters the palmitoylome and the subcellular distribution of Rho3, a major meiotic target, stimulating sexual differentiation in the absence of normal physiological cues. From this study, I conclude that PAT substrate specificity depends on enzyme levels and propose the rheostatic control of PAT activity as a mechanism by which cells shape stimuli-induced palmitoylomes. Future questions stemming from this work are also discussed

Mining real-world networks in systems biology and economics

Recent advances in biotechnology have yielded an explosion of data describing biological systems, creating rich opportunities for new insights into cellular inner-workings and therapeutic discoveries. To keep up with this rapid growth and increase in data complexity, we need novel static, integrative, and dynamic methodologies to continue mining these networked systems. In this thesis we introduce new static, integrative, and dynamic computational frameworks for network analysis, and combine existing ones in new ways, to elucidate the biotechnological biases and functional principles governing molecular interactions and their implications in disease. We focus on mining new knowledge from the yeast and human interactomes, since these are currently the most complete data in biology. We perform three lines of experimental work: 1) the macro-scale study, where we model the yeast and human interactomes and show that their interactome data are growing in structurally and functionally principled ways, characterised by a non-random dual topological nature; 2) the micro-scale study, where we zoom into the specifics of wiring patterns around individual genes and uncover a unique core sub-structure within the human interactome, which contains driver genes dubbed to be the main triggers for disease onset; and 3) the data integration study, where we introduce a new computational framework for fusing multiple types of molecular interaction data and use it to construct the first unified model of the cell’s functional organisation and cross-communication lines. Similarly, a new field of systems economics has gained recent attention, with more financial and economic network data emerging at an increasing pace. Hence, we introduce a new computational methodology for tracking network dynamics and use it to quantify the micro- and macro-scale topological changes in the world trade network over the past 50 years, and to demonstrate the fundamental relationship between topological perturbations and indicators of countries’ political and economic stabilities.Open Acces

Characterization of protein secretion in Mycobacterium leprae using phoA fusions in Escherichia coli and Mycobacterium smegmatis

Complete sequencing and annotation of the M. leprae genome has provided new information related to proteins constituting its hypothetical proteome. Since M. leprae can not be grown in vitro, novel approaches are needed to determine which proteins are expressed during infection and whether these proteins are related to pathogenesis. Secreted proteins represent a distinct group of protein with respect to their structure and function, contribution to virulence and are of particular importance for vaccine development because they are often immunogenic and have the potential to be recognized early in infection. The objectives of this study were: 1) to identify putatively secreted proteins of M. leprae based on protein sequences homologies with known MT secreted proteins; 2) to apply bioinformatic tools designed to assess proteins for secretion, to proteins selected in objective 1 with the goal of improving the likelihood that selected proteins are secreted by M. leprae, 3) to validate secretion of selected ML proteins through genetic cloning of predicted secreted ML protein genes using surrogate host bacteria, E. coli and M. smegmatis. Bioinformatics identified 24 proteins with high probability for secretion in M. leprae. Fifteen of 24 ML genes showed more than 50% amino acid homology with their M. tuberculosis counterparts and were studied for gene expression and secretion. mRNA analysis identified transcripts for all Sec-dependent pathway proteins of 15 genes predicted to be secreted in M. leprae. PhoA fusion studies in E. coli showed that 5 of 6 (83%) ML proteins (ML0091, ML0097, ML0620, ML1811 and ML1812) were secreted in E. coli and 2 of 7 (29%) proteins (ML0715 and ML2569) were secreted in M. smegmatis. Only lipoproteins were secreted in M. smegmatis suggesting the importance of mycobacterial-related characteristics for secretion of ML lipoproteins. These results suggest that bioinformatic tools are reliable predictors for identifying secreted proteins in M. leprae and support the hypothesis that Sec-dependent secretion exists in M. leprae

Multiplex Networks Structure and Dynamics

Los estudios tradicionales en teoría de redes complejas, en general, representan la interacción entre dos elementos del sistema a través de un solo enlace. Esta representación resulta ser una simplificación excesiva en la mayoría de los casos de interés práctico y puede llevar a resultados y conclusiones engañosas. Esto se debe a que la mayoría de los sistemas reales poseen una estructura multicapa, ya que en una gran cantidad de casos de estudio reales existen muchos tipos distintos de interacción entre los constituyentes del sistema. Por ejemplo, un sistema de transporte está constituido por múltiples modos de viajes; un sistema biológico incluye múltiples canales de señalización que operan en paralelo; finalmente, una red social está constituida por múltiples tipos de relaciones distintas (de trabajo, de amistad, de parentesco, etc.) que operan vía distintos modos de comunicación en paralelo (en línea, o desconectados). Para representar de manera apropiada estos sistemas, años atrás se introdujo la noción de redes multiplex en campos tan distintos como la ingeniería y la sociología, al mismo tiempo que los instrumentos analíticos desarrollados para describirlas y analizarlas fueron muy escasos. Esta escasez se debía fundamentalmente a un aspecto: aunque muchas características y métricas de las redes tradicionales (de una sola capa) están bien definidas en la teoría tradicional de redes complejas, resulta muy desafiante generalizarlas al caso de redes multicapa, incluso para aquellas que son más simples. El interés por nuevos desarrollos teóricos para es estudio en profundidad de las redes multiplex, por lo tanto, ha ido creciendo sólo en los últimos años, gracias sobre todo a la gran cantidad de datos disponibles sobre sistemas reales que necesitan de una representación multicapa si se quieren describir y entender en profundidad. En esta Tesis desarrollamos un lenguaje matemático formal para representar la redes multiplex en términos de la teoría algébrica de grafos. En particular, introducimos la noción de matriz de supra-adyacencia como generalización de la matriz de adyacencia definida en el caso de una red de una sola capa. Así mismo definimos el supra-Laplaciano de una red multiplex como generalización del Laplaciano. También, se propone una representación agregada de una red multiplex a través de la noción de grafo cociente. Esto permite asociar a la red multiplex original, un grafo de una sola capa en el cual se agregan los distintos tipos de interacciones presentes. Por un lado, a través de este procedimiento se introduce una manera bien definida de agregar capas, y por otro, también permite definir otra red, formada por las capas, que contiene toda la información relativa a la interacción entre las mismas. La importancia de las nuevas definiciones radica en que, gracias a ellas, podemos utilizar algunos teoremas y resultados de teoría espectral de grafos y sus respectivos cocientes para estudiar propiedades espectrales de redes múltiplex y su representación agregada. Finalmente, también introducimos la noción de matriz de caminos asociados a una red multiplex. En una red de una sola capa un camino es una sucesión de nodos adyacentes. En una red multiplex pueden existir distintas nociones de caminos dependiendo de la manera en que se quieran tratar los enlaces entre capas. Dada una noción de camino, a esta resultará asociada una matriz de caminos. Una vez desarrollado el lenguaje formal apto a describir una red multicapa, afrontamos el problema de la generalización de algunas medidas estructurales. En particular tratamos el caso del coeficiente de agrupamiento (tanto local como global) y la centralidad de un subgrafo. Aunque ya existían en la literatura algunas propuestas de generalización del coeficiente de agrupamiento, la mayoría de estas resultaban ser definiciones ad hoc con respecto a casos de estudios particulares, o directamente mal definidas. Las distintas medidas que proponemos en estas tesis son muy generales, bien normalizadas y se reducen a la tradicional medida de coeficiente de agrupamiento para redes de una sola capa cuando el número de capas es uno. En cuanto a la centralidad de subgrafos, utilizamos este caso particular para demonstrar la utilidad de construir sobre nociones básicas (como es la de camino) a la hora de generalizar medidas estructurales.\\ Por otro lado, mucha información respecto a la organización estructural de una red (ya sea multicapa o de una sola capa) está codificada en el espectro de la matriz de adyacencia a ella asociada así como en el del Laplaciano. Por esta razón, estudiamos las propiedades espectrales tanto de la matriz de supra-adyacencia como del supra-Laplaciano. En particular, con respecto a la matriz de supra-adyacencia, estudiamos su autovalor máximo. Éste resulta de interés ya que está en la base de medidas topológicas como la entropía de ensemble de los caminos, así como del estudio de las propiedades críticas de algunos procesos dinámicos. Por ejemplo, el valor crítico del parámetro de difusión en un modelo de propagación epidemias depende del autovalor máximo de la matriz de adyacencia. Para el estudio de este autovalor utilizamos técnicas perturbativas. Podemos definir una capa que llamamos dominante, que será aquella que tenga el mayor autovalor máximo de la matriz de adyacencia asociada a la misma. El autovalor máximo de la matriz de supra-adyacencia resulta ser igual al autovalor máximo de la capa dominante al primer orden perturbativo. Además, la corrección de segundo orden es dependiente de las correlaciones entre nodos que representan el mismo objecto en distintas capas distintas. Adicionalmente, aprovechando los resultados conocidos que relacionan el espectro de un grafo cociente con aquel de su grafo padre, estudiamos el espectro de una red multicapa a partir de su representación agregada. En particular, demostramos que los autovalores del Laplaciano de la red de capas son un subconjunto de los autovalores del supra-Laplaciano de la red multicapa, cuando todos los nodos participan en todos las capas. Este resultado nos permite estudiar la conectividad algébrica de la red multicapa, o sea el primer autovalor no-nulo y obtener algunos resultados tanto exactos como perturbativos sobre este. En concreto, las transiciones estructurales en redes multicapa son de gran interés. En esta tesis presentamos una teoría de estas transiciones que se deriva por completo de la noción de grafo cociente. Finalmente, presentamos un modelo de contagio social y estudiamos la existencia de estados meta-estables macroscópicos en los cuales una fracción finita de nodos resultan contagiados. La existencia de una capa dominante hace que sea esta la que determine el valor crítico del contagio, definido como el valor de este parámetro a partir del cual existe un estado macroscopico de la infección (también para las capas no-dominantes). Este resultado se derivada utilizando el método perturbativo para calcular el autovalor máximo de la matriz de supra-adyacencia. Simulaciones numéricas del modelo confirman los resultados analíticos. Para terminar, en el presente trabajo exponemos nuestras conclusiones a manera de resumen por un lado, y por otra, discutiendo cuáles son los aspectos que a nuestro criterio, podrían ser de interés para futuras investigaciones en este tema

Statistical mechanics of complex networks

Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks is governed by robust organizing principles. Here we review the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, we discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, as well as the interplay between topology and the network's robustness against failures and attacks.Comment: 54 pages, submitted to Reviews of Modern Physic