El establecimiento de un reservorio latente del virus del HIV en células T CD4+ es la mayor barrera contra la erradicación de esta enfermedad. Para lograr su erradicación, se necesitaría combinar la terapia antirretroviral HAART con drogas capaces de reactivar los virus durmientes. El objetivo principal de este estudio es entender cómo se establece la latencia tras la integración del virus en el genoma, con el propósito de identificar factores involucrados que pudieran ser dianas de una nueva terapia. Hemos generado una librería de clones que contienen un minigenoma latente del virus que expresa GFP como reportero cuando se reactiva. Esta librería permite estudiar la posible relación existente entre estado de la cromatina en el lugar de integración y actividad del promotor. También hemos estudiado la implicación de la interferencia transcripcional en el establecimiento de la latencia en los clones cuya integración del HIV ha tenido lugar en genes transcripcionalmente activos. Para investigar el mecanismo de represión durante la latencia, se han llevado a cabo depleciones de factores relacionados con el reensamblaje de la cromatina y proteínas relacionadas con represión transcripcional. Finalmente, hemos buscado drogas que puedan reactivar el virus latente como posible terapia a combinar con la terapia antiretroviral. / The establishment of a latent HIV reservoir in CD4+ T cells is the main barrier to prevent the eradication of the virus and converts its infection in a chronic disease. To achieve its eradication, it would be needed to combine HAART with drugs able to reactivate the dormant viruses. The main objective of this study is to understand how latency is established after proviral integration into the genome, with the aim of identifying factors involved that could be targeted by new therapeutic approaches. We have generated a library of clones containing a latent HIV minigenome that expresses GFP as a reporter only when reactivated. This library allows the study of the relationship between the chromatin state at the site of integration and HIV promoter activity. We have also studied the implication of transcriptional interference in the establishment of latency in those clones where HIV has integrated in transcriptionally active genes. To further investigate the mechanism of transcriptional repression in latency we have performed knockdowns of known chromatin reassembly factors and repression-related proteins by using shRNA expression. Finally we have searched drugs that can reactivate the latent HIV as a possible therapy to combine with HAART.

DYRK1A is the most studied member of the DYRK family of protein kinases, because is one of the human chromosoma 21 proteins for which changes in gene dosage result in neuropathological alterations. DYRKs are activated by autophosphorylation on a tyrosine residue in the activation loop, a one-off event that takes place during translation. Accordingly, DYRK1A would be constitutively active once is synthesized. However, DYRK1A is extremely sensitive to gene dosage, and thus it is predictable that not only its activity but also its actual protein amounts have to be tightly regulated by mechanisms not yet characterized. In the present study, the protein kinase NLK has been identified as a novel regulator of DYRK1A protein stability. DYRK1A interacts with NLK in physiological conditions. The interaction results in the phosphorylation of DYRK1A at multiple sites, which have been identified by mass spectrometry analysis. These phosphorylation events promote DYRK1A proteasome-dependent degradation. Moreover, DYRK1A degradation is induced by stimulating cells with Wnt1 or Wnt3a, or overexpressing elements of the Wnt signaling cascade such as the Frizzled-1 receptor or NLK activators such as HIPK2. In addition, DYRK1A interacts with and phosphorylates -catenin and TCF-4 and enhances -catenin-dependent transcriptional activity, at least by phosphorylation of -catenin. Thus, these results suggest that DYRK1A acts as a positive factor in the Wnt--catenin signaling pathway and NLK acts as a negative regulator by targeting both DYRK1A and TCF/LEF transcription factors for proteasome-mediated degradation. / DYRK1A es el miembro más estudiado de la familia de proteína quinasas DYRK, porque es una de las proteínas de la cromosoma humano 21 para la que cambios en la dosis génica dan lugar a alteraciones neuropatológicas. Las quinasas DYRK se activan por autofosforilación en un residuo tirosina localizado en el lazo de activación, un evento único que ocurre durante la traducción. Como consecuencia, DYRK1A sería constitutivamente activa una vez se ha sintetizado. Sin embargo, DYRK1A es extremadamente sensible a la dosis génica, y por tanto es predecible que no sólo su actividad, pero también los niveles de proteína han de estar estrictamente controlados por mecanismos reguladores que todavía no han sido caracterizados. En este trabajo, la proteína quinasa NLK ha sido identificada como un nuevo regulador de la estabilidad de DYRK1A. DYRK1A interacciona con NLK en condiciones fisiológicas, y la interacción tiene como resultado la fosforilación de DYRK1A en residuos serina/treonina, varios de los cuales han sido identificados por espectrometría de masas. La interacción con NLK y la subsecuente fosforilación promueven la degradación de DYRK1A vía el proteasoma. Además, la degradación de DYRK1A es inducida por estimulación de la células con Wnt1 o Wnt3a, o por sobreexpresión de miembros de la cascada de señalización de Wnt, como el receptor Frizzled-1 o de un activador de NLK como HIPK2. Finalmente, se ha demostrado que DYRK1A se une y fosforila -catenina y TCF-4. La fosforilación de, al menos, -catenina es responsable del incremento de la actividad transcripcional dependiente de esta proteína en presencia de DYRK1A. Todos estos resultados sugieren que DYRK1A actúa como un factor positivo en la vía de señalización Wnt--catenina y NLK actúa como un regulador negativo al inducir la degradación vía proteasoma no sólo de los factores de transcripción TCF/LEF sino también del modulador positivo DYRK1A.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

[Truncated abstract] Human complement receptor 2 (CR2/CD21) is a cell surface glycoprotein detected on specific cells involved in immunity, which binds complement C3 cleavage fragments, cellular ligands IFN-? and CD23 as well as the EBV coat protein, gp350/220. During the early stages of B-cell development CR2/CD21 is silenced. Expression is initiated on immature B-cells escaping negative selection. During peripheral maturation CR2/CD21 is up-regulated with B-cell sub-populations showing distinctive surface levels (comparatively low, intermediate or high). CR2/CD21 is silenced upon terminal plasmacytic differentiation. Appropriate timing and expression level of CR2/CD21 is important for the development of a healthy B-cell repertoire. Previous studies have identified sequences within the proximal promoter and first intron of CR2/CD21 that cooperate within native chromatin to control cell-specific silencing. Further, analysis of cultured human cells has revealed chromatin structural variation causing DNase I hypersensitivity at these regulatory sites in a CR2/CD21-expressing mature B-cell line (Raji) which are absent in a non-lymphoid cell type (K562). The primary focus of the present study involved characterising chromatin structural variation over previously recognized DNase I hypersensitive regions at the CR2/CD21 locus in human cells to understand how chromatin structure might regulate developmental expression of CR2/CD21. ... These studies provide evidence that notch signaling influences CR2/CD21 expression in human cell lines. First, in vivo binding of CBF1 to CR2/CD21 sequences in the proximal promoter and CRS implies that CR2/CD21 is a direct target of notch activation. Second, the effect of exogenous notch signalling molecules on CR2/CD21 proximal promoter activity was modulated by factors binding tandem E-boxes near the transcriptional start site suggesting that the notch pathway may also influence CR2/CD21 expression via control of HLH molecules. Third, initiation of CR2/CD21 expression was observed in a nonexpressing pre-B cell line (Reh) by co-culture with stromal cells expressing a notch ligand (OP9-DL) but not control stroma (OP9-GFP). Together, these findings support a role for notch regulation of B-cell maturation and invite speculation that initiation of CR2/CD21 expression following negative selection of immature B-cells involves crosstalk between HLH transcriptional regulators and the notch pathway. Furthermore, the Reh/OP9-DL co-culture system may provide a model to directly study the relationship between cell signalling molecules, transcription factor regulation, chromatin structural variation and differentiation of B-cells.

Dissertation (Ph.D.) -- University of Texas Southwestern Medical Center at Dallas, 2008. / Vita. Bibliography: p. 96-110.

Orientador: Claudia Marcia Aparecida Carareto / Banca: Marie-Anne Van Sluys / Banca: Elgion Lucio da Silva Loreto / Banca: Ivan de Godoy Maia / Banca: Maria Elisabete Jorge Amaral / Resumo: Elementos de transposição (TEs) são entidades genéticas que podem ter profundos impactos, estrutural, funcional, intra e interespecíficos na evolução dos genomas. A contribuição dos TEs para formação de novas seqüências codificadoras de proteínas é de particular interesse porque sua inserção em exons pode alterar a seqüência protéica influenciando diretamente o fenótipo. Além disso, o estudo das condições que provocam a ativação de TEs, e os mecanismos que os regulam, justifica o interesse de se identificar TEs expressos em tecidos ou condições específicas. Este estudo foca tais questões usando um modelo vegetal: C. arabica, única espécie híbrida e poliplóide do seu gênero, derivada de uma hibridização recente e natural entre C. canephora e C. eugenioides. As análises foram realizadas por meio de uma variedade de abordagens: 1) análises computacionais usando uma combinação de RepeatMasker, tBLASTx e diversas bibliotecas de TEs referência estocadas no Repbase; 2) análises de expressão baseadas em macroarranjos de DNA; e 3) avaliação do número de cópias e distribuição cromossomal de TEs ativos por Hibridização in situ fluorescente (FISH). Foram identificados 180 unigenes com fragmentos de TEs nas três espécies de café. Em uma primeira análise, com base em unigenes selecionados, foi possível sugerir 26 putativas proteínas com inserção de cassetes de TEs, demonstrando uma provável contribuição para a variabilidade do repertório protéico hospedeiro. Por outro lado, 327 ESTs similares a TEs expressos foram identificadas com uma possível abundância diferencial para duas famílias de Ty3/Gypsy (dea1 e Retrosat) identificadas em apenas duas bibliotecas de C. canephora (sementes e pericarpo). Análises de expressão mostraram que muitos dos mRNAs quiméricos e TEs ativos apresentam baixa expressão... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Transposable elements (TEs) are genetic entities that can have profound structural, intra, interspecific impacts in evolution of the genomes. The contribution of the TEs in the protein coding region is of particular interest because insertions of TE into exons can alter the protein sequence influencing directly the phenotype. Moreover, the analysis of the conditions that cause the TE activation and the mechanisms that regulate them justify the interest of identifying expressed TEs in tissues or specific conditions. This study focus such subjects using the plant model: C. arabica, unique hybrid species and polyploidy of their genus, derived of a recent and natural hybridization between C. canephora and C. eugenioides. The analyses were performed by a variety of approaches: 1) computational analyses using a combination of RepeatMasker, tBLASTx e several libraries of reference TEs stored in Repbase; 2) expression analysis based in macroarrays; and 3) evaluation of copy number e chromosomal distribution of expressed TEs by Fluorescent in situ hybridization (FISH). 180 unigenes containing TE fragments were identified in the three Coffea species. Based in selected unigenes, it was possible to identify 26 putative proteins harboring TE-cassettes, demonstrating a probable contribution for the host protein repertory variability. In addition, 327 ESTs similar to expressed TEs were identified with a probable differential abundance for two families of Ty3/Gypsy (dea1 and Retrosat) identified only in two cDNA libraries from C. canephora (seeds and pericarp). Our expression analyses showed that most of the chimerical mRNAs and expressed TEs has low or null expression. On the other hand, several transcripts had their expression reestablished in cell culture treated with Cycloheximide, a drug that permit the accumulation of transcripts previously silenced by reverting a mechanisms... (Complete abstract click electronic access below) / Doutor

The discovery of RNA in 1868 by Friedrich Miescher was meant to be the prologue to an exciting new era in Biology full of scientific breakthroughs and accomplishments. Since then, RNAs have been proven to play an indispensable role in biological processes such as coding, decoding, regulation and expression of genes. In particular, the discovery of small non-coding RNAs and especially miRNAs, in C. elegans first and thereafter to almost all animals and plants, started to fill in the puzzle of a complex gene regulatory network present within cells. The aim of this thesis is to shed more light on the features and functionality of small RNAs. In particular, we will focus on the function and biogenesis of miRNAs and piRNAs, across multiple species, by employing advanced computational methods and machine learning. We first introduce a novel method (Chimira) for the identification of miRNAs from sets of animal and plant hairpin precursors along with post-transcriptional terminal modifications that are not encoded by the genome. This method allows the characterisation of the prevalence of miRNA isoforms within different cell types and/or conditions. We have applied Chimira within a larger study that examines the effect of terminal uridylation in RNA degradation in oocytes and cells in either embryonic or adult stage. This study showed that uridylation is the predominant transcriptional regulation mechanism in oocytes while it does not retain the same functionality on mRNAs and miRNAs, both in embryonic and adult cells. We then move on to a large-scale analysis of small RNA-Seq datasets in order to identify potential modification signatures across specific conditions and cell types or tissues in Human and Mouse. We extracted the full modification profiles across 461 samples, unveiling the high prevalence of modification signatures of mainly 1 to 4 nucleotides. Additionally, samples of the same cell type and/or condition tend to cluster together based on their miRNA modification profiles while miRNA gene precursors with close genomic proximity showed a significant degree of co-expression. Finally, we elucidate the determinant factors in strand selection during miRNA biogenesis as well as update the miRBase annotation with corrected miRNA isoform sequences. Next, we introduce a novel computational method (mirnovo) for miRNA prediction from RNA-Seq data with or without a reference genome using machine learning. We demonstrate its efficiency by applying it to multiple datasets, including single cells and RNaseIII deficient samples, supporting previous studies for the existence of non-canonical miRNA biogenesis pathways. Following this, we explore and justify a novel piRNA biogenesis pathway in Mouse which is independent of the MILI enzyme. Finally, we explore the efficiency of CRISPR/Cas9 induced editing of miRNA targets based on the computationally predicted accessibility of the targeted regions in the genome. We have publicly released two web-based novel computational methods and one on-line resource with results regarding miRNA biogenesis and function. All findings presented in this study comprise another step forward within the journey of elucidation of RNA functionality and we believe they will be of benefit to the scientific community.