Plant alkaloids are secondary metabolites that may be synthesized in an inducible defense response to herbivory (Baldwin 1999). Genetic engineering of secondary metabolic pathways in plants to enhance or reduce metabolite production is limited by the current understanding of these pathways and their regulation in response to environmental conditions. This study was intended to provide new insights into the mechanism and regulation of alkaloid biosynthesis in N. tabacum by identifying genes that are coordinately regulated during conditions that induce alkaloid biosynthesis and by comparing their expression in regulatory mutant backgrounds that differ at two quantitative alkaloid loci, A and B. In order to identify novel genes that are differentially expressed during alkaloid biosynthesis, the transcriptional profiling procedure, fluorescent differential display (FDD), was used to screen total RNA isolated from Burley 21 (WT, AABB) and LA21 (low alkaloid regulatory mutant, aabb) tobacco root cultures that were induced for alkaloid synthesis. Four of thirteen cloned FDD fragments showed sequence homology to genes with defense-related functions. The differential expression of genes represented by selected FDD gene fragments was confirmed by comparing Northern blots of transcripts of those genes to known alkaloid biosynthetic genes, putrescine methyl transferase (PMT3), ornithine decarboxylase (ODC3), arginine decarboxylase (ADC1), and quinolinate phosphoribosyltransferase (QPRT). The role of the A and B loci in differential expression of genes represented by FDD clones and of known nicotine biosynthetic genes was examined using quantitative real time polymerase chain reaction (QRT-PCR) to measure transcript levels of these genes in four tobacco genotypes differing in alkaloid content, Burley 21(AABB), HI21 (AAbb), LI21(aaBB), and LA21 (aabb). Results of this study suggest that the A/B regulon is not limited to alkaloid biosynthetic genes, but includes multiple genes with defense-related functions. QRT-PCR analysis of nicotine biosynthetic genes and genes represented by confirmed differentially expressed FDD clones showed increased mRNA accumulation in response to alkaloid induction in all the tested genotypes, which suggests that the A and B mutations affect overall mRNA accumulation levels, rather than gene inducibility, per se. Baldwin, I.T. 1999. Inducible nicotine production in native Nicotiana as an example of adaptive phenotypic plasticity. Journal of Chem. Ecol. 25: 3-30. / Master of Science

A utilização de toxinas Cry de Bacillus thuringiensis (Bt) no controle de lepidópteros-praga, principalmente em áreas onde a estratégia de refúgio não é regulamentada, facilita a evolução da resistência em populações de pragas-alvo. Há três relatos de resistência à campo para Spodoptera frugiperda (J.E. Smith), dois para a toxina Cry1F e um para Cry1Ab. No Brasil, ocorrem populações resistentes à Cry1F e Cry1Ab. Esse trabalho foi voltado à identificação do mecanismo de resistência de uma população de S. frugiperda à toxina Cry1F, baseando-se nas hipóteses existentes para explicar o modo de ação de toxinas Cry. Uma dessas hipóteses é baseada na formação de poros na membrana do epitélio intestinal, enquanto a outra na transdução de sinal intracelular e ativação do processo de morte celular. Para a identificação do mecanismo de resistência de S. frugiperda à toxina Cry1F, o receptor caderina de linhagens suscetível (SUS) e resistente (RES) foi caracterizado, bem como realizado estudos de expressão gênica diferencial comparativa pela análise do transcritoma dessas linhagens. Estudos de expressão gênica diferencial comparativa também foram realizados pela análise do transcritoma do intestino de lagartas de linhagens SUS e resistente isogênica (RESiso), para a identificação do mecanismo molecular de resistência à toxina Cry1F. A caracterização do transcrito do gene caderina das linhagens suscetível, resistente e resistente isogênica revelou diferenças na composição de aminoácidos da proteína caderina predita entre as linhagens suscetível e resistente à toxina Cry1F nos domínios de repetição CR5, CR6 e CR10 e no domínio C-terminal. Também foi verificado que das mutações encontradas na linhagem RES, apenas as mutações da região C-terminal foram fixadas na linhagem RESiso. A análise comparativa do transcritoma de linhagens SUS e RES indicou a maior expressão de genes relacionados à metabolização de xenobióticos, como as monoxigenases do citocromo P450, glutationa-S-transferases e carboxilcolinesterases, em lagartas resistentes, mas não foram encontradas diferenças na expressão de receptores Cry, como aminopeptidase N e fosfatase alcalina. Porém, caderina foi superexpressa e o transportador ABCg5 teve expressão reduzida na linhagem RES. O ABCg5 foi indicado como o provável mecanismo de resistência dessa linhagem à toxina Cry1F, juntamente com o aumento da capacidade de detoxificação relatada. A análise comparativa do transcritoma de linhagens SUS e RESiso produziu resultados semelhantes à análise anterior quanto ao padrão de expressão de enzimas de detoxificação, mas nesse caso foi observada redução da transcrição de caderina na linhagem RESiso em relação à SUS. A análise da linhagem isogênica também indicou alteração na expressão de transportadores ABC na linhagem RESiso; porém, para o transportador ABCb1. A análise comparativa do transcritoma de linhagens SUS e RESiso corroborou a participação do sistema de detoxificação e acrescentou a redução na expressão do receptor caderina como mecanismo de resistência dessa população à toxina Cry1F, assim como a de transportadores ABC, apesar do transportador ABCg5 não ter sido identificado nessa análise comparativa. / The broad use of Cry toxins from Bacillus thuringiensis (Bt) to control lepidopteran pests, particularly where refuge strategies are not legalized or implemented, has facilitated the evolution of resistance of pest populations. There are three records of resistance of Spodoptera frugiperda (J.E Smith) in field condition to Bt toxins so far, two of them to Cry1F and one to Cry1Ab toxins. In Brazil, field-evolved resistance of S. frugiperda has been recorded for both toxins. Thus, we aimed to identify the mechanisms associated to the resistance of S. frugiperda to Cry1F toxin based on the two concurrent hypotheses on the mode of action of Cry toxins. One of such hypotheses is based on the potential of Cry toxins to form pores in the membrane of the gut epithelium, while the other is based on the production of an intracellular transduction signal and the activation of the process of cell death. To identify the resistance mechanisms of S. frugiperda to Cry1F toxin, we characterized the transcript of the cadherin receptor of susceptible (SUS) and resistant (RES) strains of S. frugiperda to search for mutations and performed a comparative analysis of the transcriptome from SUS and RES strains. We also analyzed the transcriptome from the gut of SUS and isogenic resistant strains (RESiso) in order to identify the molecular mechanisms associated to the resistance of S. frugiperda to Cry1F. The characterization of cadherin receptor in SUS, Res and REsiso strains showed differences in the amino acid composition of the repeated domains CR5, CR6 and CR10 and in the C-terminal domain. Only mutations occurring on C-terminal of the RES strain were maintained in the RESiso strain. The comparative transcriptome between SUS and RES strains indicated a higher expression of genes related to the detoxification process, such as cytochrome P450s, glutathione-S-transferases and carboxylcholinesterases in the RES strain, while no differences in the expression of Cry receptors, such aminopeptidade N and alkaline phosphatase, were observed. However, transcriptomic analysis indicated up-regulation of cadherin and down-regulation of the ABCg5 transporter was down-regulated in the RES strain. We propose that ABCg5 is one of the mechanisms involved in S. frugiperda resistance to Cry1F, together with the increased detoxification activity observed. Analysis of the gut transcriptome from SUS and RESiso yielded similar results regarding the differential expression of detoxifying enzymes, but on this case cadherin was down-regulated in RESiso as compared to the SUS strain. Down-regulation of ABC transporters in the RESiso strain was also observed, but for the ABCb1 transporter. Analyses of the transcriptome of SUS and RESiso strains also indicated the resistance of S. frugiperda to Cry1F is related to an increased transcription of detoxifying enzymes and a reduced transcription of the cadherin receptor. Our data also demonstrates the resistance is due to the existence of adequate constitutive levels of transcription of genes that respond to the intoxication with Cry1F.

Após o seqüenciamento completo do genoma humano, a busca e caracterização do conjunto completo de genes humanos constitui-se no principal desafio nesta área de investigação, sendo o passo limitante para o progresso na exploração dos dados contidos no seqüenciamento deste genoma. O projeto de transcriptoma denominado \"Transcript Finishing Initiative\" (TFI) surgiu neste contexto, com o objetivo principal de gerar fragmentos parciais de transcritos humanos, que não haviam sido descritos previamente e determinar sua seqüência, para iniciar a caracterização de novos genes humanos. A estratégia utilizada foi q alinhamento de todas as seqüências ORESTES e ESTs disponíveis com a seqüência pública do genoma humano e o agrupamento j c1usterização destas com base nas coordenadas deste genoma. Algumas das regiões que não eram cobertas por estas seqüências foram, então, completadas, por RT-PCR, utilizando-se primers ancorados nos clusters vizinhos. Cada par de clusters de ESTs selecionado para validação experimental foi designado como uma Unidade do \"Transcript Finishing\" (TFU), tendo sido validadas experimentalmente, pelo grupo TFI, Um total de 211 TFUs foram validadas, sendo que 197 seqüências consenso foram submetidas ao Genbank (CF272536-CF272733). Atualmente, apenas um pequeno número destas seqüências ainda são considerados genes novos, sem que haja um cDNA depositado em banco de dados; contudo para um número considerável destas TFUs não existe qualquer caracterização sobre sua função. Na tentativa de contribuir para melhor caracterização dos genes identificados no projeto TFI, e tendo, como base, a linha de pesquisa do laboratório, que busca genes diferencialmente expressos envolvidos em transformação maligna/tumorigênese, o presente trabalho propôs a utilização das seqüências TFUs para estudar sua possível associação com tumores de glia humanos e outros tipos de tumores (de próstata e de mama). Para tanto, as TFUs foram analisadas \"in silico\" para estabelecer seu grau de ineditismo como um novo gene ou um gene sem função conhecida, e, para análise de sua expressão diferencial entre tecidos normais e tumorais de cérebro, próstata e mama. Para validar estas análises computacionais na bancada, foram gerados macro- e microarranjos de DNA utilizando-se as TFUs disponíveis como clones físicos ou amplicons, para o rastreamento com sondas das linhagens celulares A172 e T98G de glioblastomas. Os resultados das análises destes dados foram confirmados por PCR quantitativo tanto nas linhagens como em amostras clínicas de astrocitomas que apresentam diversos graus de malignidade. Como resultado, foi possível organizar um Banco de Clones Físicos de TFUs, além de identificar e selecionar uma TFU (168), cuja expressão correlaciona diretamente com o grau de malignidade dos tumores de glia. Esta seqüência corresponde a um novo gene, já que não existe a seqüência de cDNA completo nos bancos de dados. Em vista disto, a TFU168 foi selecionada para estudos funcionais posteriores, que já estão em andamento, através da obtenção de suaseqüência completa de cDNA para ensaios de superexpressão e do silenciamento gênico através de RNAi. / Upon complete sequencing of the human genome, identification and characterization of the complete set of human genes constitutes the major challenge in this research field, constituting the limiting step for progress in exploration of the informations contained in the genome sequencing data. The Transcript Finishing Initiative (TFI) transcriptome project arose in this context, aiming at the generation, sequencing and characterization of partial new human transcripts and genes. The strategy adopted was the alignment of the alI the available ORESTES and EST sequences data with the public human genome sequence and their clusterization based on the coordinates of this genome. Thus, some of the regions which were not cover by ESTs and ORESTES (gaps) were then completed by RT-PCR using primers anchored in the neighboring clusters. Each pair of EST clusters selected for experimental validation was named Transcript Finishing Unit (TFU). A large number (211) of TFU s were validated and 197 -consensus sequences were submitted to the Genbank (CF272536-CF272733). At present, only a few of these sequences are considered as new genes without a full-Iength cDNA sequence deposited in the data bank, however, no functional characterization is yet available for a large number of these sequences. In an attempt to contribute to further characterization of these genes identified in the TFI project and keeping in mind the main interest of our laboratory, which is the identification of differentially expressed genes in tumor versus normal tissue, the present work aims at utilizing these TFUs to find differentially expressed genes associated with human glial tumors and with other kinds of tumors. To this end, these sequences were first subjected to in silico analysis in order to establish their degree of ineditism (new sequences and/or sequences with unknown function) and their expression profile between normal and tumoral tissues of brain, mammary gland and prostate. To validate this computational analysis, DNA macro- and microarrays were generated with the TFU sequences and screened with cDNA probes obtained from the A172 and T98G glioblastomas cell lines. The results of these screenings were confirmed by quantitative PCR both in cell lines and in tumor samples of different degrees of malignancy. The results obtained in this work allowed the organization of a TFUs Physical Clones Bank and the identification and selection of one sequence (TFU 168), whose expression is directly re1ated to the degree of tumor malignancy. This sequence constitutes a new gene, since no complete cDNA sequence is available in the data banks. Therefore, TFU168 was selected for further functional studies by obtaining its full-Iength cDNA sequence to be used for over expression by silencing this gene using RNAi.

Diese Doktorarbeit behandelt die Beobachtung, daß Koregulationsmuster in Genexpressionsdaten häufig Funktionsstrukturen der Zelle widerspiegeln. Zunächst werden simulierte Genexpressionsdaten und Expressionsdaten aus Hefeexperimenten mit Hilfe von Independent Component Analysis (ICA) und verwandten Faktormodellen untersucht. In einem eher theoretischen Zugang werden anschließend Beziehungen zwischen den Expressionsmustern und der biologischen Funktion der Gene aus einem Optimalitätsprinzip hergeleitet. Lineare Faktormodelle, beispielsweise ICA, zerlegen Genexpressionsmatrizen in statistische Komponenten: die Koeffizienten bezüglich der Komponenten können als Profile von verborgenen Variablen ("Expressionsmoden") interpretiert werden, deren Werte zwischen den Proben variieren. Im Gegensatz zu Clustermethoden beschreiben solche Faktormodelle eine überlagerung biologischer Effekte und die individuellen Reaktionen der einzelnen Gene: jedes Genprofil besteht aus einer überlagerung der Expressionsmoden, die so die gemeinsamen Schwankungen vieler Gene erklären. Die linearen Komponenten werden blind, also ohne zusätzliches biologisches Wissen, aus den Daten geschätzt, und die meisten der hier betrachteten Methoden erlauben es, nahezu schwach besetzte Komponenten zu rekonstruieren. Beim Ausdünnen einer Komponente werden Gene sichtbar, die stark auf die entsprechende Mode reagieren, ganz in Analogie zu Genen, die differentielle Expression zwischen einzelnen Proben zeigen. Verschiedene Faktormodelle werden in dieser Arbeit auf simulierte und experimentelle Expressionsdaten angewendet. Bei der Simulation von Expressionsdaten wird angenommen, daß die Genexpression von einigen unbeobachteten Variablen ("biologischen Expressionsmoden") abhängt, die den Zellzustand beschreiben und deren Einfluss auf die Gene sich durch nichtlineare Funktionen, die sogenannten Genprogramme, beschreiben läßt. Besteht Hoffnung, solche Expressionsmoden durch blinde Datenanalyse wiederzufinden? Die Tests in dieser Arbeit zeigen, daß die Moden mit ICA recht zuverlässig gefunden werden, selbst wenn die Daten verrauscht oder leicht nichtlinear sind und die Anzahl der wahren und der geschätzten Komponenten nicht übereinstimmt. Regressionsmodelle werden an Profile einzelner Gene angepasst, um ihre Expression durch Expressionsmoden aus Faktormodellen oder durch die Expression einzelner Transkriptionsfaktoren zu erklären. Nichtlineare Genprogramme werden mit Hilfe von nichtlinearer ICA ermittelt: solche effektiven Genprogramme könnten zur Beschreibung von Genexpression in großen Zellmodellen Verwendung finden. ICA und verwandte Methoden werden auf Expressionsdaten aus Zellzyklusexperimenten angewendet: neben biologisch interpretierbaren Moden werden experimentelle Artefakte identifiziert, die vermutlich Hybridisierungseffekte oder eine Verunreinigung der Proben widerspiegeln. Für einzelne Komponenten wird gezeigt, daß die koregulierten Gene gemeinsame biologische Funktionen besitzen und daß die entsprechenden Enzyme bevorzugt in bestimmten Bereichen des Stoffwechselnetzes zu finden sind. Die Expressionmechanismen scheinen also - als Ergebnis der Evolution - Funktionsbeziehungen zwischen den Genen widerzuspiegeln: es wäre unter ökonomischen Gesichtspunkten vermutlich ineffizient, wenn kooperierende Gene nicht auch koreguliert würden. Um diese teleologische Vorstellung von Genexpression zu formalisieren, wird in dieser Arbeit ein mathematisches Modell zur Analyse der optimalen differentiellen Expression (ANODE) vorgeschlagen: das Modell beschreibt Regulatoren, also beispielsweise Gene oder Enzyme, und die von ihnen gesteuerten Variablen, zum Beispiel metabolische Flüsse. Das Systemverhalten wird durch eine Fitnessfunktion bewertet, die beispielsweise vom bestimmten Stoffwechselflüssen abhängt und die es zu optimieren gilt. Dieses Optimalitätsprinzip definiert eine optimale Reaktion der Regulatoren auf kleine äußeren Störungen. Zur Berechnung optimaler Regulationsmuster braucht das zu regulierende System nur teilweise bekannt zu sein: es genügt, sein mögliches Verhalten in der Nähe des optimalen Zustandes sowie die lokale Form der Fitnesslandschaft zu kennen. Die Methode wird auf zeitabhängige Störungen erweitert: um die Antwort von Stoffwechselsystemen auf kleine oszillatorische Störungen zu beschreiben, werden frequenzabhängige Kontrollkoeffizienten definiert und durch Summations- und Konnektivitätstheoreme charakterisiert. Um die vorhergesagte Beziehung zwischen Expression und Funktion zu prüfen, werden Kontrollkoeffizienten für ein großes Stoffwechselnetz simuliert, und ihre statistischen Eigenschaften werden untersucht: die Struktur der Kontrollkoeffizientenmatrix bildet die Netztopologie ab, das bedeutet, chemische Reaktionen haben gewöhnlich einen geringen Einfluss auf weit entfernte Teile des Netzes. Außerdem hängen die Kontrollkoeffizienten innerhalb eines Teilnetzes nur schwach von der Modellierung des umgebenden Netzes ab. Verschiedene plausible Annahmen über sinnvolle Expressionsmuster lassen sich formal aus dem Optimalitätsprinzip herleiten: das Hauptergebnis ist eine allgemeine Beziehung zwischen dem Verhalten und der biologischen Funktion von Regulatoren, aus der sich zum Beispiel die Koregulation von Enzymen in Komplexen oder Funktionsmodulen ergibt. Die Funktionen der Gene werden in diesem Zusammenhang durch ihre linearen Einflüsse (die sogenannten Responsekoeffizienten) auf fitnessrelevante Zellvariable beschrieben. Für Stoffwechselenzyme werden aus den Theoremen der metabolischen Kontrolltheorie Summenregeln hergeleitet, die die Expressionsmuster mit der Struktur des Stoffwechselnetzes verknüpfen. Weitere Vorhersagen betreffen eine symmetrische Kompensation von Gendeletionen und eine Beziehung zwischen Genexpression und dem Fitnessverlust aufgrund von Deletionen. Wenn die optimale Steuerung durch eine Rückkopplung zwischen Zellvariablen und den Regulatoren verwirklicht ist, dann spiegeln sich funktionale Beziehungen auch in den Rückkopplungskoeffizienten wider. Daher ist zu erwarten, daß Gene mit ähnlicher Funktion durch Eingangssignale aus denselben Signalwegen gesteuert werden. Das Modell der optimalen Steuerung sagt voraus, daß Expressionsprofile aus Linearkombinationen von Responsekoeffizientenprofilen bestehen: Tests mit experimentellen Expressionsdaten und simulierten Kontrollkoeffizienten stützen diese Hypothese, und die gemeinsamen Komponenten, die aus diesen beiden Arten von Daten geschätzt werden, liefern ein anschauliches Bild der Stochwechselvorgänge, die zur Anpassung an unterschiedliche Umgebungen notwendig sind. Alles in allem werden in dieser Arbeit empirische Beziehungen zwischen der Expression and der Funktion von Genen bestätigt. Darüber hinaus werden solche Beziehungen aus theorischen Gründen vorhergesagt. Ein Hauptziel ist es, teleologische Aussagen über Genexpression auf explizite Annahmen zurückzuführen und dadurch klarer zu formulieren, und so einen theoretischen Rahmen für die Integration von Expressionsdaten und Funktionsannotationen zu liefern. Während andere Autoren die Expression mit Funktionskategorien der Gene oder topologisch definierten Stoffwechselwegen verglichen haben, schlage ich vor, die Funktionen von Genen durch ihre Responsekoeffizienten auszudrücken. Als ein Hauptergebnis dieser Arbeit werden allgemeine Beziehungen zwischen der Funktion, der optimalen Expression und dem Programm eines Gens vorhergesagt. Soweit die Optimalitätsannahme gilt, rechtfertigt das Modell die Verwendung von Expressionsdaten zur Funktionsannotation und zur Rekonstruktion von Stoffwechselwegen und liefert außerdem eine funktionsbezogene Interpretation für die linearen Komponenten in Expressionsdaten. Die Methoden aus dieser Arbeit sind nicht auf Genexpressionsdaten beschränkt: die Faktormodelle lassen sich auch auf Protein- und Metabolitdaten anwenden, und das Optimalitätsprinzip könnte ebenfalls auf andere Steuerungsmechanismen angewendet werden, beispielsweise auf die allosterische Steuerung von Enzymen. / This thesis is concerned with the observation that coregulation patterns in gene expression data often reflect functional structures of the cell. First, simulated gene expression data and expression data from yeast experiments are studied with independent component analysis (ICA) and with related factor models. Then, in a more theoretical approach, relations between gene expression patterns and the biological function of the genes are derived from an optimality principle. Linear factor models such as ICA decompose gene expression matrices into statistical components. The coefficients with respect to the components can be interpreted as profiles of hidden variables (called "expression modes") that assume different values in the different samples. In contrast to clusterings, such factor models account for a superposition of effects and for individual responses of the different genes: each gene profile consists of a superposition of the expression modes, which thereby account for the common variation of many genes. The components are estimated blindly from the data, that is, without further biological knowledge, and most of the methods considered here can reconstruct almost sparse components. Thresholding a component reveals genes that respond strongly to the corresponding mode, in comparison to genes showing differential expression among individual samples. In this work, different factor models are applied to simulated and experimental expression data. To simulate expression data, it is assumed that gene expression depends on several unobserved variables ("biological expression modes") which characterise the cell state and that the genes respond to them according to nonlinear functions called "gene programs". Is there a chance to reconstruct such expression modes with a blind data analysis? The tests in this work show that the modes can be found with ICA even if the data are noisy or weakly nonlinear, or if the numbers of true and estimated components do not match. Regression models are fitted to the profiles of single genes to explain their expression by expression modes from factor models or by the expression of single transcription factors. Nonlinear gene programs are estimated by nonlinear ICA: such effective gene programs may be used for describing gene expression in large cell models. ICA and similar methods are applied to expression data from cell-cycle experiments: besides biologically interpretable modes, experimental artefacts, probably caused by hybridisation effects and contamination of the samples, are identified. It is shown for single components that the coregulated genes share biological functions and the corresponding enzymes are concentrated in particular regions of the metabolic network. Thus the expression machinery seems to portray - as an outcome of evolution - functional relationships between the genes: regarding the economy of resources, it would probably be inefficient if cooperating genes were not coregulated. To formalise this teleological view on gene expression, a mathematical model for the analysis of optimal differential expression (ANODE) is proposed in this work: the model describes regulators, such as genes or enzymes, and output variables, such as metabolic fluxes. The system´s behaviour is evaluated by a fitness function, which, for instance, rates some of the metabolic fluxes in the cell and which is supposed to be optimised. This optimality principle defines an optimal response of regulators to small external perturbations. For calculating the optimal regulation patterns, the system to be controlled needs to be known only partially: it suffices to predefine its possible behaviour around the optimal state and the local shape of the fitness function. The method is extended to time-dependent perturbations: to describe the response of metabolic systems to small oscillatory perturbations, frequency-dependent control coefficients are defined and characterised by summation and connectivity theorems. For testing the predicted relation between expression and function, control coefficients are simulated for a large-scale metabolic network and their statistical properties are studied: the structure of the control coefficients matrix portrays the network topology, that is, chemical reactions tend to have little control on distant parts of the network. Furthermore, control coefficients within subnetworks depend only weakly on the modelling of the surrounding network. Several plausible assumptions about appropriate expression patterns can be formally derived from the optimality principle: the main result is a general relation between the behaviour of regulators and their biological functions, which implies, for example, the coregulation of enzymes acting in complexes or functional modules. In this context, the functions of genes are quantified by their linear influences (called ``response coefficients'') on fitness-relevant cell variables. For enzymes controlling metabolism, the theorems of metabolic control theory lead to sum rules that relate the expression patterns to the structure of the metabolic network. Further predictions concern a symmetric compensation for gene deletions and a relation between gene expression and the fitness loss caused by gene deletions. If optimal regulation is realised by feedback signals between the cell variables and the regulators, then functional relations are also portrayed in the linear feedback coefficients, so genes of similar function may be expected to share inputs from the same signalling cascades. According to the model of optimal regulation, expression profiles are linear combinations of response coefficient profiles: tests with experimental expression profiles and simulated control coefficients support this hypothesis, and the common components which are estimated from both kinds of data provide a vivid picture of the metabolic adaptations that are required in different environments. To summarise, empirical relations between gene expression and function have been confirmed in this work. Furthermore, such relations have been predicted on theoretical grounds. A main aim is to clarify teleological assertions about gene expression by deriving them from explicit assumptions, and thus to provide a theoretical framework for the integration of expression data and functional annotations. While other authors have compared expression to functional gene categories or topologically defined metabolic pathways, I propose to relate it to the response coefficients. A main result of this work is that general relations are predicted between a gene's function, its optimal expression behaviour, and its regulatory program. Where the assumption of optimality is valid, the model justifies the use of expression data for functional annotation and pathway reconstruction, and it provides a function-related interpretation for the linear components behind expression data. The methods from this work are not limited to gene expression data: the factor models are applicable to protein and metabolite data as well, and the optimality principle may also apply to other regulatory mechanisms, such as the allosteric control of enzymes.

O Brasil ocupa a segunda posição mundial na produção de carne bovina, a implantação de novas ferramentas para a seleção de animais zebuínos (Bos indicus) com carne de melhor qualidade tem uma importante contribuição para a competividade da pecuária de corte. Neste contexto, compreender os padrões de expressão de microRNAs específicos envolvidos nos processos que afetam a maciez da carne é fundamental para a sua produção, uma vez que essa característica organoléptica é de grande valor na aceitação deste alimento pelos consumidores. O advento das tecnologias de sequenciamento de nova geração em conjunto com o uso de ferramentas de bioinformática tem permitido o estudo do genoma em larga escala de forma mais rápida e com menor custo. Para este estudo, foram utilizadas amostras do músculo Longissimus dorsi de 34 animais da raça Nelore com medidas extremas de valor genético estimado (EBV) para força de cisalhamento (FC). Os RNAs totais foram extraídos, as bibliotecas de microRNA foram construídas e os sequenciamentos foram realizados em equipamento da plataforma Illumina (MiSeq). O processamento dos dados foi feito por meio dos softwares FastQC, Cutadapt e miRDeep2 e as análises de expressão diferencial foram realizadas por meio do programa estatístico QuasiSeq. Utilizando um critério de taxa de descoberta de falsos positivos (FDR) inferior a 0,1, três microRNAs (bta-mir-182, bta-mir-183, bta-mir-338) foram identificados como diferencialmente expressos entre os grupos de animais com valores extremos de EBV para FC. Um total de 1204 genes alvos foi previsto e análises funcionais de enriquecimento foram realizadas por ferramentas de bioinformática. Várias redes e vias metabólicas como a sinalização de apoptose e regulação dos mecanismos celulares pela protease calpaína foram obtidas, demonstrando assim que os genes alvos identificados estariam envolvidos em muitos processos metabólicos relacionados com a maciez da carne bovina. / Brazil occupies the second world position in beef production and thus, the implementation of new tools to select zebuine animals (Bos indicus) with better beef quality has an important contribution to the competitiveness of beef cattle. Inside this context, to comprehend the microRNAs expression patterns involved in the processes that are related to beef tenderness is essential to the meat production since this organoleptic characteristic has a high value in meat acceptance by the consumers. The advent of new generation sequencing technologies along with the biotechnology tools usage has allowed large-scale genome studies as well as faster and cheaper analysis. In this study, samples of the Longissimus dorsi muscle from 34 animals of Nellore cattle breed with extreme estimated genetic value (EBV) for shear force (FC) were used. The total RNAs were extracted, libraries of microRNA were built and finally the sequencing was performed using the Illumina (MiSeq) platform equipment. Data processing was done using FastQC, Cutadapt and miRDeep2 softwares while the differential expression analyzes were realized through the statistical package QuasiSeq. Using a false discovery rate (FDR) criteria below 0.1, three microRNAs (bta-mir- 182, bta-mir-183, bta-mir-338) were identified as differentially expressed among the group of animals with extreme EBV values for FC. A total of 1024 target genes were predicted and functional analyzes of enrichment were performed using bioinformatics tools. Many metabolic networks and pathways such as the apoptosis signalization and cell regulation mechanisms by calpain protease were obtained, demonstrating therefore that the identified target genes would be involved in many metabolic processes related with the beef tenderness.

<p>cDNA microarrays is one of the first high-throughput gene expression technologies that has emerged within molecular biology for the purpose of functional genomics. cDNA microarrays compare the gene expression levels between cell samples, for thousands of genes simultaneously. </p><p>The microarray technology offers new challenges when it comes to data analysis, since the thousands of genes are examined in parallel, but with very few replicates, yielding noisy estimation of gene effects and variances. Although careful image analyses and normalisation of the data is applied, traditional methods for inference like the Student <i>t</i> or Fisher’s <i>F</i>-statistic fail to work.</p><p>In this thesis, four papers on the topics of empirical Bayes and full Bayesian methods for two-channel microarray data (as e.g. cDNA) are presented. These contribute to proving that empirical Bayes methods are useful to overcome the specific data problems. The sample distributions of all the genes involved in a microarray experiment are summarized into prior distributions and improves the inference of each single gene.</p><p>The first part of the thesis includes biological and statistical background of cDNA microarrays, with an overview of the different steps of two-channel microarray analysis, including experimental design, image analysis, normalisation, cluster analysis, discrimination and hypothesis testing. The second part of the thesis consists of the four papers. Paper I presents the empirical Bayes statistic <i>B</i>, which corresponds to a <i>t</i>-statistic. Paper II is based on a version of <i>B</i> that is extended for linear model effects. Paper III assesses the performance of empirical Bayes models by comparisons with full Bayes methods. Paper IV provides extensions of <i>B</i> to what corresponds to <i>F</i>-statistics.</p>

cDNA microarrays is one of the first high-throughput gene expression technologies that has emerged within molecular biology for the purpose of functional genomics. cDNA microarrays compare the gene expression levels between cell samples, for thousands of genes simultaneously. The microarray technology offers new challenges when it comes to data analysis, since the thousands of genes are examined in parallel, but with very few replicates, yielding noisy estimation of gene effects and variances. Although careful image analyses and normalisation of the data is applied, traditional methods for inference like the Student t or Fisher’s F-statistic fail to work. In this thesis, four papers on the topics of empirical Bayes and full Bayesian methods for two-channel microarray data (as e.g. cDNA) are presented. These contribute to proving that empirical Bayes methods are useful to overcome the specific data problems. The sample distributions of all the genes involved in a microarray experiment are summarized into prior distributions and improves the inference of each single gene. The first part of the thesis includes biological and statistical background of cDNA microarrays, with an overview of the different steps of two-channel microarray analysis, including experimental design, image analysis, normalisation, cluster analysis, discrimination and hypothesis testing. The second part of the thesis consists of the four papers. Paper I presents the empirical Bayes statistic B, which corresponds to a t-statistic. Paper II is based on a version of B that is extended for linear model effects. Paper III assesses the performance of empirical Bayes models by comparisons with full Bayes methods. Paper IV provides extensions of B to what corresponds to F-statistics.

The recent advent of High-Throughput Sequencing (HTS) methods has triggered a revolution in gene regulation studies. Demand has never been higher to process the immense amount of emerging data to gain insight into the regulatory mechanisms of the cell. We address this issue by describing methods to analyze, integrate and interpret HTS data from different sources. In particular, we developed and benchmarked Pyicos, a powerful toolkit that offers flexibility, versatility and efficient memory usage. We applied it to data from ChIP-Seq on progesterone receptor in breast cancer cells to gain insight into regulatory mechanisms of hormones. Moreover, we embedded Pyicos into a pipeline to integrate HTS data from different sources. In order to do so, we used data sets from ENCODE to systematically calculate signal changes between two cell lines. We thus created a model that accurately predicts the regulatory outcome of gene expression, based on epigenetic changes in a gene locus. Finally, we provide the processed data in a Biomart database to the scientific community. / La llegada reciente de nuevos métodos de High-Throughput Sequencing (HTS) ha provocado una revolución en el estudio de la regulación génica. La necesidad de procesar la inmensa cantidad de datos generados, con el objectivo de estudiar los mecanismos regulatorios en la celula, nunca ha sido mayor. En esta tesis abordamos este tema presentando métodos para analizar, integrar e interpretar datos HTS de diferentes fuentes. En particular, hemos desarollado Pyicos, un potente conjunto de herramientas que ofrece flexibilidad, versatilidad y un uso eficiente de la memoria. Lo hemos aplicado a datos de ChIP-Seq del receptor de progesterona en células de cáncer de mama con el fin de investigar los mecanismos de la regulación por hormonas. Además, hemos incorporado Pyicos en una pipeline para integrar los datos HTS de diferentes fuentes. Hemos usado los conjuntos de datos de ENCODE para calcular de forma sistemática los cambios de señal entre dos líneas celulares. De esta manera hemos logrado crear un modelo que predice con bastante precisión los cambios de la expresión génica, basándose en los cambios epigenéticos en el locus de un gen. Por último, hemos puesto los datos procesados a disposición de la comunidad científica en una base de datos Biomart.

Submitted by Alison Vanceto (alison-vanceto@hotmail.com) on 2017-05-12T13:15:41Z No. of bitstreams: 1 TeseEBC.pdf: 4307606 bytes, checksum: 49ebc853f6c4c152639d651f942f72b8 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-05-18T20:31:37Z (GMT) No. of bitstreams: 1 TeseEBC.pdf: 4307606 bytes, checksum: 49ebc853f6c4c152639d651f942f72b8 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-05-18T20:31:43Z (GMT) No. of bitstreams: 1 TeseEBC.pdf: 4307606 bytes, checksum: 49ebc853f6c4c152639d651f942f72b8 (MD5) / Made available in DSpace on 2017-05-25T18:43:38Z (GMT). No. of bitstreams: 1 TeseEBC.pdf: 4307606 bytes, checksum: 49ebc853f6c4c152639d651f942f72b8 (MD5) Previous issue date: 2016-04-18 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Odorant-binding proteins (OBPs) are of great importance for survival and reproduction since they participate in initial steps of the olfactory signal transduction cascade, solubilizing and transporting chemical signals to the olfactory receptors. A comparative analysis of OBPs between closely related species may help explain how these genes evolve and are maintained under natural selection and how differences in these proteins can affect olfactory responses, and consequently lead to species differentiation. We studied OBP genes in the closely related species Anastrepha fraterculus and Anastrepha obliqua, which, albeit generalists, have different host preferences, using transcriptomes and real time quantitative PCR data. We identified 24 different OBP sequences from Anastrepha fraterculus and 25 from A. obliqua, which correspond to 21 Drosophila melanogaster OBP genes. Phylogenetic analysis separated Anastrepha OBPs sequences in four branches that represent four subfamilies: classic, minus-C, plus-C and dimer. We found evidence of positive selection in three classic subfamily genes OBP56h-1, OBP56h-2 e OBP57c and in the plus-C subfamily gene OBP50a, and at least one duplication event that preceded the speciation of these two species. Four positively selected sites putatively resulted in radical changes in amino acid properties. Inferences on tertiary structures of putative proteins from these genes revealed that at least one positively selected change involves the binding cavity (the odorant binding region) in the plus-C OBP50a, which is important because changes in the binding cavity could change OBPs specificity. Differential gene expression analysis at different reproductive stages showed that all nine OBP genes tested were significantly differentially expressed between A. fraterculus and A. obliqua at several reproductive profiles, but OBP56a, OBP56d, OBP57c and both OBP56h paralogs showed the highest differences in expression levels. The results generated in this study indicated that at least seven OBP genes may be involved in the A. fraterculus e A. obliqua differentiation, and in the fraterculus group differentiation as well. / As proteínas ligantes a odores (OBPs – odorant-binding proteins) são de grande importância para a sobrevivência e reprodução, pois participam do passo inicial da cascata de transdução dos sinais olfatórios, solubilizando e transportando os sinais químicos (odores e feromônios) até os receptores olfativos. A análise comparativa dos genes OBPs entre espécies próximas pode ajudar na compreensão de como o repertório desses genes é mantido sob seleção natural, além de fornecer informações acerca de como as diferenças observadas podem afetar as respostas olfatórias e, consequentemente, levar à diferenciação dessas espécies. Estudamos genes OBP em duas espécies-irmãs Anastrepha fraterculus e Anastrepha obliqua, as quais têm preferência por diferentes frutos hospedeiros, usando dados de transcriptomas e de PCR quantitativa. Identificamos 24 sequências OBP para A. fraterculus e 25 para A. obliqua, que corresponderam a 21 genes OBP de Drosophila melanogaster. Análises filogenéticas separaram as OBPs de Anastrepha em quatro ramos, que representam quatro subfamílias dessa família gênica: classic, minus-C, plus-C e dimer. Evidências de seleção positiva foram observadas nos genes da subfamília classic OBP56h-1, OBP56h-2 e OBP57c, e para o gene da subfamília plus-C OBP50a, e pelo menos um evento de duplicação gênica que precede a especiação dessas duas espécies. Quatro sítios selecionados positivamente resultavam em mudanças radicais nas propriedades dos aminoácidos. Inferências utilizando a estrutura terciária predita para essas OBPs revelaram que pelo menos um desses sítios faz parte da cavidade ligante ao odor de OBP50a, sendo que uma mudança nessa região pode alterar a especificidade de uma OBP. Análises de expressão por PCR quantitativa em diferentes estágios reprodutivos das moscas mostraram que todos os nove genes testados possuíam expressão gênica significativamente diferente entre A. fraterculus e A. obliqua para mais de um perfil reprodutivo, sendo que OBP56a, OBP56d, OBP57c e os dois parálogos OBP56h foram os que mais apresentaram diferenças entre as duas espécies. Todos os resultados gerados pelo presente trabalho indicam que pelo menos sete genes OBP podem estar envolvidos na diferenciação entre A. fraterculus e A. obliqua e, potencialmente, na diferenciação do grupo fraterculus. / FAPESP: 2012/17160-8. / CAPES: 99999.004252/2014-04

O Brasil ocupa a segunda posição mundial na produção de carne bovina, a implantação de novas ferramentas para a seleção de animais zebuínos (Bos indicus) com carne de melhor qualidade tem uma importante contribuição para a competividade da pecuária de corte. Neste contexto, compreender os padrões de expressão de microRNAs específicos envolvidos nos processos que afetam a maciez da carne é fundamental para a sua produção, uma vez que essa característica organoléptica é de grande valor na aceitação deste alimento pelos consumidores. O advento das tecnologias de sequenciamento de nova geração em conjunto com o uso de ferramentas de bioinformática tem permitido o estudo do genoma em larga escala de forma mais rápida e com menor custo. Para este estudo, foram utilizadas amostras do músculo Longissimus dorsi de 34 animais da raça Nelore com medidas extremas de valor genético estimado (EBV) para força de cisalhamento (FC). Os RNAs totais foram extraídos, as bibliotecas de microRNA foram construídas e os sequenciamentos foram realizados em equipamento da plataforma Illumina (MiSeq). O processamento dos dados foi feito por meio dos softwares FastQC, Cutadapt e miRDeep2 e as análises de expressão diferencial foram realizadas por meio do programa estatístico QuasiSeq. Utilizando um critério de taxa de descoberta de falsos positivos (FDR) inferior a 0,1, três microRNAs (bta-mir-182, bta-mir-183, bta-mir-338) foram identificados como diferencialmente expressos entre os grupos de animais com valores extremos de EBV para FC. Um total de 1204 genes alvos foi previsto e análises funcionais de enriquecimento foram realizadas por ferramentas de bioinformática. Várias redes e vias metabólicas como a sinalização de apoptose e regulação dos mecanismos celulares pela protease calpaína foram obtidas, demonstrando assim que os genes alvos identificados estariam envolvidos em muitos processos metabólicos relacionados com a maciez da carne bovina. / Brazil occupies the second world position in beef production and thus, the implementation of new tools to select zebuine animals (Bos indicus) with better beef quality has an important contribution to the competitiveness of beef cattle. Inside this context, to comprehend the microRNAs expression patterns involved in the processes that are related to beef tenderness is essential to the meat production since this organoleptic characteristic has a high value in meat acceptance by the consumers. The advent of new generation sequencing technologies along with the biotechnology tools usage has allowed large-scale genome studies as well as faster and cheaper analysis. In this study, samples of the Longissimus dorsi muscle from 34 animals of Nellore cattle breed with extreme estimated genetic value (EBV) for shear force (FC) were used. The total RNAs were extracted, libraries of microRNA were built and finally the sequencing was performed using the Illumina (MiSeq) platform equipment. Data processing was done using FastQC, Cutadapt and miRDeep2 softwares while the differential expression analyzes were realized through the statistical package QuasiSeq. Using a false discovery rate (FDR) criteria below 0.1, three microRNAs (bta-mir- 182, bta-mir-183, bta-mir-338) were identified as differentially expressed among the group of animals with extreme EBV values for FC. A total of 1024 target genes were predicted and functional analyzes of enrichment were performed using bioinformatics tools. Many metabolic networks and pathways such as the apoptosis signalization and cell regulation mechanisms by calpain protease were obtained, demonstrating therefore that the identified target genes would be involved in many metabolic processes related with the beef tenderness.