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Mitochondrial Control of Gene Expression and Extrinsic Apoptosis

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Mitochondrial Control of Gene Expression and Extrinsic Apoptosis Mitochondrial control of gene expression and extrinsic apoptosis Juan D´ıaz-Colunga Madrid, 2019 Investigar para mejorar nuestraMitochondrial control of gene expression and extrinsic apoptosis Author Juan D´ıaz-Colunga Supervisors Francisco J. Iborra and Raul´ Guantes PhD program Condensed Matter Physics, Nanoscience and Biophysics Universidad Autonoma´ de Madrid Contents Abstract i 1 Introduction 1 1.1 The Systems Biology approach . 1 1.2 Modeling in Cellular Biology . 3 1.2.1 Modeling: why and how? . 3 1.2.2 Deterministic kinetic models . 6 1.2.3 Probabilistic formulation of reaction kinetics . 8 1.2.4 Stochastic kinetic models . 13 1.3 Cell-to-cell variability . 15 1.3.1 Understanding cell-to-cell variability . 16 1.3.2 Implications . 19 1.4 Mitochondrial variability . 22 1.4.1 Mitochondria and metabolism . 22 1.4.2 The origins of mitochondrial variability . 24 1.4.3 Mitochondrial variability and gene expression . 26 1.4.4 Implications . 32 2 Mitochondrial control of gene expression 35 2.1 Global and specific constraints on gene expression . 35 2.2 Connecting mitochondrial and transcriptional variability . 43 2.2.1 RNA-seq and data processing . 43 2.2.2 Expression changes at the gene level . 46 2.2.3 Alternative splicing . 49 2.3 Transcript-specific regulation of production and degradation rates . 53 2.3.1 Time series RNA-seq and data processing . 53 2.3.2 Quantification of transcript degradation rates . 53 2.3.3 Asymmetric scaling of transcription and degradation . 60 2.4 Discussion and perspectives . 62 3 Mitochondrial control of extrinsic apoptosis 65 3.1 Variability in the apoptotic response . 65 3.2 Mitochondrial and apoptotic variability . 69 3.2.1 Mitochondrial discrimination of cell fate and death time . 69 3.2.2 Mitochondria and apoptotic gene expression . 74 3.3 Modeling apoptosis . 78 3.3.1 The Extrinsic Apoptosis Reaction Model . 78 3.3.2 Including mitochondrial regulation: the mitoEARM . 79 3.4 Keys to the mitochondrial regulation of apoptosis . 87 3.5 Discussion and perspectives . 92 4 Conclusions 97 Bibliography 103 List of Figures 117 List of Tables 119 Appendices 121 A Experimental methods 123 B Log-normal co-sampling 127 Abstract Even under homogeneous environmental conditions, cells with identical genotypes can display significant variations at the phenotypic level, that is, differences in size, mor- phology and internal state. This is a result of the genetic and signaling circuits that control cellular functions being subject to fluctuations in the levels of their components. Cell-to-cell variability is regarded as an essential agent in many key cellular activities such as development, differentiation, evolution, virus infection or cell death, and has been shown to serve a biological function in many cases. Thus, tracing back its sources is central to understand the behaviors of individual cells and ultimately act on them. In this work we use a combination of experimental, mathematical and computational tools to investigate the role of mitochondria (the main energy provider in eukaryotic cells) on the generation of gene expression noise. Gene expression is highly energy demanding and in turn determines phenotype, pointed as the cause of variable individual cellular responses in several processes, notably apoptosis. Heterogeneity in apoptotic outcomes is particularly relevant as it poses the main cause of tumor resistance to chemotherapy. Our results highlight the importance of mitochondria as a global modulator of gene ex- pression, but also reveal its role regulating complex, non-linear processes like alternative splicing. We found that this control of gene expression is specially important in the apop- totic signaling pathway: mitochondria exhibit the power to discriminate apoptotic fates at the single cell level, making it a good candidate for a biomarker of the susceptibility of cancer cells to death-inducing treatments. i Resumen Incluso en condiciones ambientales homog´eneas, c´elulas con genotipos id´enticos pueden presentar variaciones significativas a nivel fenot´ıpico,es decir, diferencias en tama˜no,morfolog´ıay estado interno. Esto es el resultado de los circutos gen´eticosy de se˜nalizaci´onque controlan la funci´oncelular estando sometidos a fluctuaciones en los niveles de sus componentes. La variabilidad de c´elulaa c´elulaes considerada un agente esencial en multitud de actividades celulares como el desarrollo, diferenciaci´on, evoluci´on,infecci´onv´ıricao la muerte celular. Por tanto, identificar sus fuentes es cen- tral para entender los comportamientos de c´elulasindividuales y en ´ultimainstancia actuar sobre ellos. En este trabajo utilizamos una combinaci´onde herramientas exper- imentales, matem´aticasy computacionales para investigar el papel de la mitocondria (principal proveedor de energ´ıa en c´elulaseucariotas) en la generaci´onde ruido en expresi´ongen´etica. La expresi´ongen´eticaes costosa energ´eticamentey determina el fenotipo, se˜naladoen muchos procesos como causa de la variabilidad en las respuestas de c´elulasindividuales, en particular en el de apoptosis. La heterogeneidad en los re- sultados apopt´oticoses particularmente relevante porque supone la principal causa de resistencia de tumores a quimioterapia. Nuestros resultados resaltan la importancia de la mitocondria como modulador global de la expresi´ong´enica,pero tambi´enrevelan su papel regulando procesos complejos y no lineales como el splicing alternativo. Encon- tramos que este control de la expresi´ong´enicaes especialmente importante en la ruta de se˜nalizaci´onapopt´otica:la mitocondria muestra el potencial de discriminar destinos apopt´oticosa nivel de c´elula´unica,convirti´endolaen un buen candidato a biomarcador de la susceptibilidad de c´elulascancer´ıgenas frente a tratamientos inductores de apop- tosis. iii 1 Introduction There are several aspects that motivate this work. First, the emergence of high- throughput technologies, along with the development of powerful statistical tools, has allowed for the systematic assessment of large sets of molecules and made quantitative information more accessible than ever for biologists. This has fueled a paradigm shift in biological research. Second, it is becoming increasingly clear that cell-to-cell differ- ences play a critical role in many biological processes, but the origins of this differences are still not completely understood. Third, the realization that many of the factors that cause cell-to-cell variability are affected by energetic constraints has put the focus in the main source of energy for eukaryotic cells: mitochondria. In this chapter, we will present the systems biology framework and discuss why quantitative modeling is central to it, reviewing some of the most important mathemat- ical tools used to build models. We will also introduce cell-to-cell variability and some of its sources and implications. Finally, we will discuss how understanding and char- acterizing mitochondrial variability can be key to trace back the origins of cell-to-cell heterogeneity. 1.1 The Systems Biology approach Biological networks All biological entities have interactions with one another at many different scales, from the molecules in a cell to the species in an ecosystem. Biological systems are often represented as networks formed by nodes and links between them. The identity of these nodes and interactions is variable: biological networks exist at the genetic, metabolic, neurological or ecological levels to name a few.1 In the context of cellular biology, networks are based on different biochemical processes serving a variety of purposes: 1 Metabolic networks are defined by the enzymes converting substrates into products and by the metabolites converted by such enzymes, as well as the interactions be- tween them. Through metabolism, cells acquire the energy and materials needed for survival and reproduction. Transcriptional networks represent the regulation of genes by transcription factors (TF). From a biochemical point of view, the structure of these networks is de- termined by the TF binding sites. The operation of these networks shapes the cell’s gene expression landscape. Protein-protein interaction networks represent physical interactions such as binding and complex formation, molecular modifications (mainly phosphorylation) and activation or inhibition of biochemical reactions. These networks allow cells to process information enabling, for example, rapid stress responses or intercellular communication. Despite cellular components being highly interconnected, reductionism has domi- nated biological research for a long time. For many decades in the latter half of the 20th century, studies in molecular and cellular biology were devoted to the generation of in- formation about the chemical composition and functions of individual cell components, that is, specific network nodes. The emergence and fast development of the -omics fields (genomics, transcriptomics, proteomics, metabolomics...) in the 21st century has greatly accelerated this process. The rise of the -omics fields The suffix -omics is used to designate many of the emerging fields of data-rich biology. These terms refer to a global, un-targeted assessment of large sets of molecules, rather than a study of each one of them individually. For instance, genomics (the first -omics discipline to appear) focuses on whole genomes, as opposed to genetics which aims to understand the role of single genes. The increasing availability of quantitative data is due to the development of techno- logical advances that enable fast
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