Seleção de características é um tópico muito importante em aplicações de reconhecimento de padrões, especialmente em bioinformática, cujos problemas são geralmente tratados sobre um conjunto de dados envolvendo muitas variáveis e poucas observações. Este trabalho analisa aspectos de seleção de características no problema de identificação de redes de regulação gênica a partir de sinais de expressão gênica. Particularmente, propusemos um modelo de redes gênicas probabilísticas (PGN) que devolve uma rede construída a partir da aplicação recorrente de algoritmos de seleção de características orientados por uma função critério baseada em entropia condicional. Tal critério embute a estimação do erro por penalização de amostras raramente observadas. Resultados desse modelo aplicado a dados sintéticos e a conjuntos de dados de microarray de Plasmodium falciparum, um agente causador da malária, demonstram a validade dessa técnica, tendo sido capaz não apenas de reproduzir conhecimentos já produzidos anteriormente, como também de produzir novos resultados. Outro aspecto investigado nesta tese é o fenômeno da predição intrinsecamente multivariada (IMP), ou seja, o fato de um conjunto de características ser um ótimo caracterizador dos objetos em questão, mas qualquer de seus subconjuntos propriamente contidos não conseguirem representá-los de forma satisfatória. Neste trabalho, as condições para o surgimento desse fenômeno foram obtidas de forma analítica para conjuntos de 2 e 3 características em relação a uma variável alvo. No contexto de redes de regulação gênica, foram obtidas evidências de que genes alvo de conjuntos IMP possuem um enorme potencial para exercerem funções vitais em sistemas biológicos. O fenômeno conhecido como canalização é particularmente importante nesse contexto. Em dados de microarray de melanoma, constatamos que o gene DUSP1, conhecido por exercer função canalizadora, foi aquele que obteve o maior número de conjuntos de genes IMP, sendo que todos eles possuem lógicas de predição canalizadoras. Além disso, simulações computacionais para construção de redes com 3 ou mais genes mostram que o tamanho do território de um gene alvo pode ter um impacto positivo em seu teor de IMP com relação a seus preditores. Esta pode ser uma evidência que confirma a hipótese de que genes alvo de conjuntos IMP possuem a tendência de controlar diversas vias metabólicas cruciais para a manutenção das funções vitais de um organismo. / Feature selection is a crucial topic in pattern recognition applications, especially in bioinformatics, where problems usually involve data with a large number of variables and small number of observations. The present work addresses feature selection aspects in the problem of gene regulatory network identification from expression profiles. Particularly, we proposed a probabilistic genetic network model (PGN) that recovers a network constructed from the recurrent application of feature selection algorithms guided by a conditional entropy based criterion function. Such criterion embeds error estimation by penalization of rarely observed patterns. Results from this model applied to synthetic and real data sets obtained from Plasmodium falciparum microarrays, a malaria agent, demonstrate the validity of this technique. This method was able to not only reproduce previously produced knowledge, but also to produce other potentially relevant results. The intrinsically multivariate prediction (IMP) phenomenon has been also investigated. This phenomenon is related to the fact of a feature set being a nice predictor of the objects in study, but all of its properly contained subsets cannot predict such objects satisfactorily. In this work, the conditions for the rising of this phenomenon were analitically obtained for sets of 2 and 3 features regarding a target variable. In the gene regulatory networks context, evidences have been achieved in which target genes of IMP sets possess a great potential to execute vital functions in biological systems. The phenomenon known as canalization is particularly important in this context. In melanoma microarray data, we verified that DUSP1 gene, known by having canalization function, was the one which composed the largest number of IMP gene sets. It was also verified that all these sets have canalizing predictive logics. Moreover, computational simulations for generation of networks with 3 or more genes show that the territory size of a target gene can contribute positively to its IMP score with regard to its predictors. This could be an evidence that confirms the hypothesis stating that target genes of IMP sets are inclined to control several metabolic pathways essential to the maintenance of the vital functions of an organism.

The detection and analysis of biomolecules, such as proteins, are of great interest since these molecules are fundamental for life and our health. Due to the complexity of biological processes, there is a great advantage of studying proteins in their natural context, for example by using bioimaging. The objective of this doctoral thesis has been to develop, implement and evaluate techniques for the use of proteinspecific affinity reagents in diverse bioimaging platforms for analysis of protein expression in situ in cells and tissues. To be able to visualize a desired protein in situ using affinity reagents, reporter labels are needed. A novel technique for labeling of antibodies on solid phase was developed. This method offers simultaneous purification, concentration and labeling of an antibody sample, giving highly predictable and reproducible results, in a miniaturized format. Another study demonstrates the use of an alternative affinity reagent, the Affibody molecule, in bioimaging as well as other immunoassays. As a relevant proof-of-principle, an Affibody molecule binding the HER2 receptor was site-specificly labeled and employed for analysis of HER2 protein expression in cells and tissue using immunofluorescence (IF), immunohistochemistry (IHC), immunoprecipitation and flow cytometry. Furthermore, it is shown how antibody-based bioimaging approaches can be applied for systematic analysis of protein expression in terms of subcellular localization and expression levels in cell lines. The systematic subcellular localization of nearly 500 proteins was performed using IF and confocal microscopy. Global analysis of expression levels of nearly 2000 proteins in a panel of cell lines using IHC and automated image analysis, revealed that most proteins are expressed in a cell size dependent manner. Two normalization approaches were evaluated and found to allow for protein profiling across the panel of morphologically diverse cells, revealing patterns of protein over- and underexpression, and proteins with stable as well as with lineage specific expression were identified. Finally, the value of antibody-based, bioimaging proteomics as a platform for biomarker discovery is demonstrated. The identification and in depth study of a candidate biomarker for colorectal cancer, SATB2, is described using both IHC and IF bioimaging. Results from extended analyses of tumor biopsies showed that detection of SATB2 protein using IHC provides a clinically relevant diagnostic tool with high specificity and sensitivity to aid in diagnosis of colorectal cancer. Furthermore, the study demonstrated a potential prognostic role of SATB2, as decreased expression was associated with a significantly shorter overall survival in patients with advanced colorectal cancer. / QC 20100824

Una dieta scorretta incrementa il rischio di malattie come l’insulino resistenza e l’obesità. Questa tesi ha l’obiettivo di valutare l’effetto di diete sbilanciate sulla fisiologia ed espressione genica in topi e suini allo svezzamento. Topi C57BL/6 sono stati sacrificati dopo 2 settimane, dopo essere stati alimentati con dieta iper-lipidica e dieta controllo. L’espressione genica è stata stimata usando la tecnologia microarray. Quattro dei sette geni identificati differenzialmente espressi tra il controllo e l’iper-lipidico sono coinvolti nella regolazione della via metabolica del sistema circadiano, che recentemente è stato mostrato avere effetti sul metabolismo lipidico e processo infiammatorio. Il secondo studio ha avuto lo scopo di capire gli effetti dello svezzamento con o senza l’aggiunta di acidificante nella dieta. I suinetti allo svezzamento (T0) sono stati comparati con i suinetti dopo una settimana (T1). Il gruppo post-svezzamento è stato alimentato con una dieta convenzionale, e metà di questi hanno ricevuto un supplemento di acido sorbico. L’aggiunta di acido sorbico nella dieta non ha causato nessuna differenza a livello fisiologico e di espressione genica. 205 geni sono stati identificati come differenzialmente espressi in T1 comparato con T0, evidenziando una forte risposta all’adattamento metabolico e agli stress subiti durante lo svezzamento. / An incorrect diet increases the risk of diseases as insulin resistance and obesity. This thesis aims at assessing the effects of unbalanced diets on gut physiology and gene expression in pig and mouse during weaning. The first research explored the impact of a high fat diet in C57BL/6 mice. High-fat-fed mice and control-fed mice were sacrificed after two weeks of treatment. Gene expression level was assessed by 90K Combimatrix microarray technology. Four of seven genes found differentially expressed between control and high fat diet mice are involved in the regulatory pathway of the circadian clock system, which was recently shown to affect lipid metabolism and inflammatory processes. Those genes were successfully validated by real time PCR. The second study aimed at understanding the weaning effect with or without acidifier addition in the diet. Piglets at weaning (T0) were compared to piglets after one week (T1). The post-weaning group was fed a conventional diet, half of which received in addition sorbic acid. The sorbic acid supplementation evidenced no effects in terms of physiology and gene expression. 205 genes were significantly differentially expressed in T1 when compared with T0, evidencing a response to the metabolic adaptation and the stress suffered during weaning.

In the last decade, molecular biology has expanded from a reductionist view to a systems-wide view that tries to unravel the complex interactions of cellular components. Owing to the emergence of high-throughput technology it is now possible to interrogate entire genomes at an unprecedented resolution. The dimension and unstructured nature of these data made it evident that new methodologies and tools are needed to turn data into biological knowledge. To contribute to this challenge we exploited the wealth of publicly available high-throughput genomics data and developed bioinformatics methodologies focused on extracting information at the pathway rather than the single gene level. First, we developed Gene Set Variation Analysis (GSVA), a method that facilitates the organization and condensation of gene expression profiles into gene sets. GSVA enables pathway-centric downstream analyses of microarray and RNA-seq gene expression data. The method estimates sample-wise pathway variation over a population and allows for the integration of heterogeneous biological data sources with pathway-level expression measurements. To illustrate the features of GSVA, we applied it to several use-cases employing different data types and addressing biological questions. GSVA is made available as an R package within the Bioconductor project. Secondly, we developed a pathway-centric genome-based strategy to reposition drugs in type 2 diabetes (T2D). This strategy consists of two steps, first a regulatory network is constructed that is used to identify disease driving modules and then these modules are searched for compounds that might target them. Our strategy is motivated by the observation that disease genes tend to group together in the same neighborhood forming disease modules and that multiple genes might have to be targeted simultaneously to attain an effect on the pathophenotype. To find potential compounds, we used compound exposed genomics data deposited in public databases. We collected about 20,000 samples that have been exposed to about 1,800 compounds. Gene expression can be seen as an intermediate phenotype reflecting underlying dysregulatory pathways in a disease. Hence, genes contained in the disease modules that elicit similar transcriptional responses upon compound exposure are assumed to have a potential therapeutic effect. We applied the strategy to gene expression data of human islets from diabetic and healthy individuals and identified four potential compounds, methimazole, pantoprazole, bitter orange extract and torcetrapib that might have a positive effect on insulin secretion. This is the first time a regulatory network of human islets has been used to reposition compounds for T2D. In conclusion, this thesis contributes with two pathway-centric approaches to important bioinformatic problems, such as the assessment of biological function and in silico drug repositioning. These contributions demonstrate the central role of pathway-based analyses in interpreting high-throughput genomics data. / En l'última dècada, la biologia molecular ha evolucionat des d'una perspectiva reduccionista cap a una perspectiva a nivell de sistemes que intenta desxifrar les complexes interaccions entre els components cel•lulars. Amb l'aparició de les tecnologies d'alt rendiment actualment és possible interrogar genomes sencers amb una resolució sense precedents. La dimensió i la naturalesa desestructurada d'aquestes dades ha posat de manifest la necessitat de desenvolupar noves eines i metodologies per a convertir aquestes dades en coneixement biològic. Per contribuir a aquest repte hem explotat l'abundància de dades genòmiques procedents d'instruments d'alt rendiment i disponibles públicament, i hem desenvolupat mètodes bioinformàtics focalitzats en l'extracció d'informació a nivell de via molecular en comptes de fer-ho al nivell individual de cada gen. En primer lloc, hem desenvolupat GSVA (Gene Set Variation Analysis), un mètode que facilita l'organització i la condensació de perfils d'expressió dels gens en conjunts. GSVA possibilita anàlisis posteriors en termes de vies moleculars amb dades d'expressió gènica provinents de microarrays i RNA-seq. Aquest mètode estima la variació de les vies moleculars a través d'una població de mostres i permet la integració de fonts heterogènies de dades biològiques amb mesures d'expressió a nivell de via molecular. Per il•lustrar les característiques de GSVA, l'hem aplicat a diversos casos usant diferents tipus de dades i adreçant qüestions biològiques. GSVA està disponible com a paquet de programari lliure per R dins el projecte Bioconductor. En segon lloc, hem desenvolupat una estratègia centrada en vies moleculars basada en el genoma per reposicionar fàrmacs per la diabetis tipus 2 (T2D). Aquesta estratègia consisteix en dues fases: primer es construeix una xarxa reguladora que s'utilitza per identificar mòduls de regulació gènica que condueixen a la malaltia; després, a partir d'aquests mòduls es busquen compostos que els podrien afectar. La nostra estratègia ve motivada per l'observació que els gens que provoquen una malaltia tendeixen a agrupar-se, formant mòduls patogènics, i pel fet que podria caldre una actuació simultània sobre múltiples gens per assolir un efecte en el fenotipus de la malaltia. Per trobar compostos potencials, hem usat dades genòmiques exposades a compostos dipositades en bases de dades públiques. Hem recollit unes 20.000 mostres que han estat exposades a uns 1.800 compostos. L'expressió gènica es pot interpretar com un fenotip intermedi que reflecteix les vies moleculars desregulades subjacents a una malaltia. Per tant, considerem que els gens d'un mòdul patològic que responen, a nivell transcripcional, d'una manera similar a l'exposició del medicament tenen potencialment un efecte terapèutic. Hem aplicat aquesta estratègia a dades d'expressió gènica en illots pancreàtics humans corresponents a individus sans i diabètics, i hem identificat quatre compostos potencials (methimazole, pantoprazole, extracte de taronja amarga i torcetrapib) que podrien tenir un efecte positiu sobre la secreció de la insulina. Aquest és el primer cop que una xarxa reguladora d'illots pancreàtics humans s'ha utilitzat per reposicionar compostos per a T2D. En conclusió, aquesta tesi aporta dos enfocaments diferents en termes de vies moleculars a problemes bioinformàtics importants, com ho son el contrast de la funció biològica i el reposicionament de fàrmacs "in silico". Aquestes contribucions demostren el paper central de les anàlisis basades en vies moleculars a l'hora d'interpretar dades genòmiques procedents d'instruments d'alt rendiment.

Aufgrund ihres sehr hohen Metastasierungsrisikos weisen Patienten mit klarzelligem Nierenzellkarzinom (kzNZK) eine sehr hohe Sterblichkeit auf. Mit den zurzeit zur Verfügung stehenden klinischen Parametern kann der Krankheitsverlauf der Patienten nach der operativen Entfernung des Primärtumors nur unzureichend vorhergesagt werden. Um das Nachsorge- und Therapieregime der Patienten zu optimieren, muss die Vorhersagegenauigkeit der bestehenden Prognosemodelle durch molekulare Marker erhöht werden. Um geeignete Gene für eine Abschätzung von Metastasierungsrisiko und krankheitsfreiem Überleben (DFS) zu identifizieren, wurden genomweite Expressionsanalysen sowohl an Lungenmetastasen (n = 24) als auch an Primärtumoren (n = 24) des kzNZK vorgenommen. Durch Vergleich von Metastasensubgruppen, die sich nach unterschiedlich langen DFS entwickelt hatten bzw. Primärtumoren, die nach unterschiedlich langen DFS Metastasen bedingten, wurden tumorintrinsische DFS-assoziierte Expressionsmuster identifiziert. Weiterhin wurden Gene identifiziert, deren Expression sich zwischen Primärtumoren unterschied, die im Krankheitsverlauf manifeste Metastasen bedingten und solchen, die dies nicht taten. Die differenzielle Expression funktionell interessanter, teilweise auch in anderen publizierten Microarraystudien an kzNZK bestätigter Gene wurde im Folgenden mittels quantitativer Polymerasekettenreaktion (qPCR) validiert. Anschließend wurde die Assoziation ausgewählter Gene mit klinischen Parametern und dem Überleben der Patienten untersucht. Ein von klinischen Parametern unabhängiger Einfluss auf den Krankheitsverlauf der Patienten wurde dabei für EDNRB und PECAM1 auf Expressionsebene (qPCR; n = 86) sowie für TSPAN7 auf Proteinebene (Immunhistochemie an „Tissue Microarrays“; n = 106) belegt. EDNRB und PECAM1 waren signifikant höher exprimiert in Primärtumoren mit günstigen klinischen Parametern (TNMI/II, G1/2, V0, N0/M0). TSPAN7 war vorwiegend in den Gefäßen der primären kzNZK nachweisbar; eine signifikant höhere Zahl TSPAN7-positiver Gefäße war ebenfalls in Tumoren mit günstigen klinischen Parametern zu verzeichnen (pT1/2, TNMI/II, N0). Überlebensanalysen zeigten ein signifikant längeres DFS für Patienten mit einer hohen im Vergleich zu solchen mit einer geringen EDNRB-Expression und für Patienten, die in beiden untersuchten Gewebestanzen der „Tissue Microarrays“ TSPAN7-positive Gefäße aufwiesen im Vergleich zu Patienten mit nur einer oder keiner TSPAN7-gefäßpositiven Stanze. Für Patienten mit einer hohen im Vergleich zu solchen mit einer geringen EDNRB- bzw. PECAM1-Expression oder mit zwei im Vergleich zu keiner oder einer TSPAN7-gefäßpositiven Gewebestanze war zudem ein signifikant längeres tumorspezifisches Überleben (TSS) zu verzeichnen. Mit Hilfe multivariater Cox-Regressionsanalysen wurde eine unabhängige günstige prognostische Relevanz für EDNRB auf das DFS sowie für EDNRB, PECAM1 und TSPAN7 auf das TSS nachgewiesen. Somit sind diese molekularen Faktoren geeignet, um die Genauigkeit der bestehenden und ausschließlich auf klinischen Parametern basierenden Prognosemodelle zu erhöhen. Für eine Abschätzung von DFS und Metastasierungsrisiko erscheint dabei insbesondere EDNRB geeignet. / Patients with clear cell renal cell carcinoma (ccRCC) have an extremely poor prognosis due to their high risk of metastases. Currently used clinico-patological parameters are insufficient for reliable prediction of metastatic risk and disease free survival (DFS) after surgical resection of the primary tumor. Molecular markers are strongly needed to improve outcome prediction, and thus to optimize the follow up and treatment schedule for patients with ccRCC. To identify genes which are suitable for the prediction of metastatic risk and DFS, genome-wide expression analyses were performed on pulmonary metastases (n = 24) and primary tumors (n = 24) obtained from patients with ccRCC. Tumor-intrinsic DFS-associated expression patterns were observed by comparing subgroups of metastases, which had developed within different DFS as well as primary tumors, which had caused metastases after different DFS. Furthermore, genes differentially expressed in primary tumors, which caused macroscopic metastases and tumors, which did not were identified. The differential expression of genes with a potential function in metastatic spread, which has in part been identified in independent published microarray studies as well, were validated by quantitative polymerase chain reaction (qPCR). Moreover, an independent prognostic impact on the survival of ccRCC patients was observed for the EDNRB und the PECAM1 gene expression (qPCR; n = 86) as well as for the TSPAN7 protein level (immunohistochemistry on tissue microarrays; n = 106). Primary tumors of patients with favourable clinico-pathological parameters (TNMI/II, G1/2, V0, N0/M0) showed a significantly higher EDNRB und PECAM1 gene expression than those with unfavorable parameters. TSPAN7 was predominantly detected in blood vessels of ccRCC tissues. In patients with favourable clinico-pathological parameters (pT1/2, TNMI/II, N0) a significantly higher number of TSPAN7-positive vessels was observed. Using survival analyses, a significantly longer DFS was observed for patients with a high compared to those with a low EDNRB expression as well as for patients with TSPAN7-positive vessels in both cores compared to no or one of the both cores investigated on tissue microarrays. A significantly longer TSS was observed for patients with a high EDNRB or PECAM1 expression as well as for patients with TSPAN7-positive vessles in both tissue cores investigated. Furthermore, EDNRB was an independent prognostic factor for the DFS of the patients; EDNRB, PECAM and TSPAN7 had an independent prognostic impact on the TSS. Therefore, these molecular markers are suitable to improve the accuracy of outcome prediction based on clinico-pathological parameters in ccRCC. For the prediction of DFS and metastatic risk EDNRB is particularly interesting.

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