Chemotherapy is commonly used in cancer treatments, however only 25% of cancers are responsive and a significant proportion develops resistance. The p53 tumour suppressor is crucial for cancer development and therapy, but has been less amenable to therapeutic applications due to the complexity of its action reflected in 67,000 papers describing its function. Here we provide a systematic approach to integrate this information by constructing large-scale logical models of the p53 interactome using extensive database and literature integration. Initially we generated models using manual curation to demonstrate the feasibility of the approach. This was followed by creation of the next generation models by automatic text mining results retrieval. Final model PKT205/G3 was generated by choosing the size of the interactome that could be analysed with current available computing power and by linking upstream nodes to input environmental signals such as DNA damage and downstream nodes to output signal such as apoptosis. This final version of the PKT205/G3 model contains 205 nodes representing genes or proteins, DNA damage input and apoptosis output, and 677 logical interactions. Predictions from in silico knock-outs and steady state model analysis were validated using literature searches and in vitro experiments. We identify an up regulation of Chk1, ATM and ATR pathways in p53 negative cells and 58 other predictions obtained by knockout tests mimicking mutations. The comparison of model simulations with microarray data demonstrated a significant rate of successful predictions ranging between 52 % and 71 % depending on the cancer type. Growth factors and receptors FGF2, IGF1R, PDGFRB and TGFA were identified as factors contributing selectively to the control of U2OS osteosarcoma and HCT116 colon cancer cell growth. In summary, we provide the proof of principle that this versatile and predictive model has vast potential for use in cancer treatment by identifying pathways in individual patients that contribute to tumour growth, defining a sub population of “high” responders and identification of shifts in pathways leading to chemotherapy resistance.

The fitness and macromolecular composition of the gram-negative bacterium E.coli are governed by a seemingly insurmountable level of complexity. However, simple phenomenological measures may be found that describe its systems-level response to a variety of inputs. This thesis explores phenomenological approaches providing accurate quantitative descriptions of complex systems in E.coli. Chapter 1 examines the relationship between unnecessary protein production and growth rate in E.coli. It was previously unknown whether the negative effects on growth rate due to multiple unnecessary protein fractions would add linearly or collectively to produce a nonlinear response. Within the regime of this thesis, it appears that the interplay between growth rate and protein is consistent with a non-interacting model. We do not need to account for complex interaction between system components. Appendix A describes a novel technique for real-time measurement of messenger RNA in single living E.coli cells. Using this technique, one may accurately describe the transcriptional response of gene networks in single cells. / Physics

Indiana University-Purdue University Indianapolis (IUPUI) / Regulatory protein families such as transcription factors (TFs) and RNA Binding Proteins (RBPs) are increasingly being appreciated for their role in regulating the respective targeted genomic/transcriptomic elements resulting in dynamic transcriptional (TRNs) and post-transcriptional regulatory networks (PTRNs) in higher eukaryotes. The mechanistic understanding of these two regulatory network types require a high resolution tissue-specific functional annotation of both the proteins as well as their target sites. This dissertation addresses the need to uncover the tissue-specific regulatory networks in development and disease. This work establishes multiple computational genomics based approaches to further enhance our understanding of regulatory circuits and decipher the associated mechanisms at several layers of biological processes. This study potentially contributes to the research community by providing valuable resources including novel methods, web interfaces and software which transforms our ability to build high-quality regulatory binding maps of RBPs and TFs in a tissue specific manner using multi-omics datasets. The study deciphered the broad spectrum of temporal and evolutionary dynamics of the transcriptome and their regulation at transcriptional and post transcriptional levels. It also advances our ability to functionally annotate hundreds of RBPs and their RNA binding sites across tissues in the human genome which help in decoding the role of RBPs in the context of disease phenotype, networks, and pathways. The approaches developed in this dissertation is scalable and adaptable to further investigate the tissue specific regulators in any biological systems. Overall, this study contributes towards accelerating the progress in molecular diagnostics and drug target identification using regulatory network analysis method in disease and pathophysiology.

Indiana University-Purdue University Indianapolis (IUPUI) / The post genomic era has propelled us to the view that the biological systems are complex network of interacting genes, proteins and small molecules that give rise to biological form and function. The past decade has seen the advent of number of new technologies designed to study the biological systems on a genome wide scale. These new technologies offers an insight in to the activity of thousands of genes and proteins in cell thereby changed the conventional reductionist view of the systems. However the deluge of data surpasses the analytical and critical abilities of the researches and thereby demands the development of new computational methods. The challenge no longer lies in the acquisition of expression profiles, but rather in the interpretation for the results to gain insights into biological mechanisms. In three different case studies, we applied various system biology techniques on publicly available and in-house genomics and proteomics data set to identify sub-network signatures. In First study, we integrated prior knowledge from gene signatures, GSEA and gene/protein network modeling to identify pathways involved in colorectal cancer, while in second, we identified plasma based network signatures for Alzheimer's disease by combining various feature selection and classification approach. In final study, we did an integrated miRNA-mRNA analysis to identify the role of Myeloid Derived Stem Cells (MDSCs) in T-Cell suppression.

La surface des vignobles mondiaux représente environ 7,9 millions ha, ce qui correspond à une production annuelle de 67 millions de tonnes de baies. La production mondiale annuelle de vins est de l’ordre de 300 millions hl/an. La surface du vignoble français est de 843 000 ha. La viticulture moderne doit affronter trois défis majeurs interdépendants : réduire l’utilisation des produits phytosanitaires, s'adapter au changement climatique, maîtriser la qualité et la typicité pour garder ou conquérir de nouveaux marchés.En 2007, la vigne est devenue la première espèce fruitière pérenne dont le génome a été séquencé. Cette avancée scientifique ouvre de nombreuses perspectives en termes de génomique fonctionnelle (ensemble de méthodes permettant de caractériser la fonction des gènes) et de biologie intégrative (ensemble de méthodes visant à appréhender le fonctionnement global de la plante et ses réponses à l’environnement). Ces perspectives dépendent pour une bonne part de la maîtrise de quantités importantes de données qu’il convient d’organiser et de corréler grâce à des outils informatiques adaptés.Des approches fonctionnelles concernant des gènes candidats et des approches transcriptomiques à haut débit ont permis d’identifier certains gènes ou certaines familles de gènes impliqués dans le développement et la maturation de la baie de raisin, mais au moment où cette thèse a débuté, aucun travail de biologie intégrative n’avait été entrepris.Le travail présenté ici, qui décrit l’obtention et l’analyse de métadonnées transcriptomiques et biochimiques portant sur la réponse de la baie à l’environnement radiatif, s’inscrit dans ce contexte. En procédant à un effeuillage partiel après la véraison, nous avons modulé l’exposition des baies au rayonnement solaire. Ceci a permis d’étudier l’influence du rayonnement (baie exposée, non exposée), de la position de la grappe (est, ouest) et de la position de la baie (à l’extérieur ou à l’intérieur de la grappe). Des baies ont été récoltées à 5 moments différents après l’effeuillage et utilisées pour des analyses métabolomiques et transcriptomiques. Leur contenu en sucres, acides organiques, acides aminés, anthocyanes et flavonols a été analysé par des dosages enzymatiques et par chromatographie liquide à haute performance). L’expression des gènes a été étudiée avec des microarrays représentatifs de l’ensemble du génome de la vigne (29600 gènes) pour les conditions présentant les différences métaboliques les plus marquées (baies exposées, situées à l’ouest et à l’extérieur de la grappe vs baies non exposées, situées à l’est et à l’intérieur de la grappe). Des analyses statistiques et corrélatives ont été conduites pour (a) déterminer les métabolites qui répondent au traitement et les facteurs qui les influencent (b) déterminer les gènes qui répondent aux traitements et ceux qui semblent co-régulés (c) préciser les réseaux de gènes et de métabolites qui semblent reliés. L’effeuillage n’affecte pas la teneur en sucres ou en acide tartrique des baies, il affecte peu les acides aminés, mais il augmente la teneur en flavonols et diminue la teneur en acide malique. Il affecte plus particulièrement les gènes associés au stress abiotique, au métabolisme secondaire, au transport et au métabolisme hormonal. Des expériences complémentaires ont permis d’identifier divers gènes spécifiquement associés à la composante thermique de l’exposition au soleil, parmi lequels des gènes codant pour des HSP, des transporteurs ABC, et des enzymes du métabolisme flavonoïdique. Des réseaux reliant des gènes et des métabolites ont pu être construits, qui associent des métabolites secondaires à des gènes de fonctions connues, ou à de nouveaux gènes candidats dont il conviendra d’étudier la fonction précise. / The total surface of vineyards worldwide is about 7.9 millions ha, which corresponds to an annual production of 67 millions tons berries. The annual world production of wines is about 300 millions hl/year. The French wineyard occupies 843 000 ha, among which 481 000 ha are dedicated to high quality wines (VQPRD) and 362 000 ha to table wines. Modern viticulture must deal with three major and related challenges : reduce the use of organic and inorganic phytochemicals, adapt the vineyard to climatic change and control the quality and the typicity in order to keep or gain new markets.In 2007, the grapevine became the first perennial fruit species whose genome was sequenced. This scientific breakthrough opens new pespectives in terms of functional genomics (set of methods allowing to characterize the function of genes) and integrative biology (set of methods allowing to study the global functioning of the plant and its response to the environment). These perspectives mainly depend on our ability to analyze large sets of data with adequate informatic tools.Functional approaches on candidate genes, and high throughput transcriptomic approaches have allowed to identify some genes or some gene families involved in the development and ripening of the grape berry, but when this Ph. D work started, no paper based on integrative biology was published on grapevine. The present work, which describes the collection and analysis of transcriptomic and metabolomic metadata related to the response of the berry to sun exposure. The exposure of the berries to the sun was controlled through a partial defoliation after veraison. This allowed to study the effects of sun exposure (exposed or shaded berries), of the position of the cluster (east, west) and of the anatomical position of the berry (outside or inside the berry). Berries were collected at 5 different time points after defoliation and used for metabolomic and transcriptomic analysis. Their content in sugars, amino acids, organic acids, anthocyanins and flavonols was analyzed by enzymatic assays and high performance liquid chromatography. For the berries whose metabolic content differed the most (exposed, west and outside berries vs shaded, east and inside berries), gene expression was studied with microarrays bearing a set of probes covering the whole genome of grapevine (29600 genes). Correlative and statistical analysis were conducted in order to (a) determine the metabolites that are the most responsive to the treatment, and the most important factors that control them (b) determine the genes that respond to the treatment and seem to be co-regulated (c) to precise the networks of genes and metabolites which seem related. Defoliation does not affect the sugar and tartaric acid contents, hardly affects amino acids, but it increases flavonol content and decreases malic acid content. It affects more specifically genes associated with abiotic stress, secondary metabolism, transport and hormonal metabolism. Additional experiments allowed us to identify genes that are specifically associated with the thermal component of sun exposure, among which genes encoding HSP, ABC transporters, and enzymes of flavonoid metabolism. Networks relating genes and metabolites could be constructed. These networks associate secondary metabolites with genes of known function and new candidate genes for which the function will have to be precised.

The understanding of cellular metabolism has been an intriguing challenge in classical cellular biology for decades. Essentially, cellular metabolism can be viewed as a complex system of enzyme-catalysed biochemical reactions that produces the energy and material necessary for the maintenance of life. In modern biochemistry, it is well-known that these reactions group into metabolic pathways so as to accomplish a particular function in the cell. The identification of these metabolic pathways is a key step to fully understanding the metabolic capabilities of a given organism. Typically, metabolic pathways have been elucidated via experimentation on different organisms. However, experimental findings are generally limited and fail to provide a complete description of all pathways. For this reason it is important to have mathematical models that allow us to identify and analyze metabolic pathways in a computational fashion. This is precisely the main theme of this thesis. We firstly describe, review and discuss existent mathematical/computational approaches to metabolic pathways, namely stoichiometric and path finding approaches. Then, we present our initial mathematical model named the Beasley-Planes (BP) model, which significantly improves on previous stoichiometric approaches. We also illustrate a successful application of the BP model to optimally disrupt metabolic pathways. The main drawback of the BP model is that it needs as input extra pathway knowledge. This is especially inappropriate if we wish to detect unknown metabolic pathways. As opposed to the BP model and stoichoimetric approaches, this issue is not found in path finding approaches. For this reason a novel path finding approach is built and examined in detail. This analysis serves us as inspiration to build the Improved Beasley-Planes (IBP) model. The IBP model incorporates elements of both stoichometric and path finding approaches. Though somewhat less accurate than the BP model, the IBP model solves the issue of extra pathway knowledge. Our research clearly demonstrates that there is a significant chance of developing a mathematical optimisation model that underlies many/all metabolic pathways.

Araucaria angustifolia é uma espécie de conífera native do Brasil, com importância econômica, social e ecológica. Devido a sua intensa exploração ao longo dos anos, atualmente a espécie cobre apenas 2% do sua área florestal original. Neste sistema, a embriogênese somática pode ser integrada em programas de melhoramento e conservação. Além disso, a similaridade entre a embriogênese zigótica e somática tem sido usada para desenvolver estudos baseados em biologia de sistemas, a fim de otimizar o desenvolvimento do embrião somático in vitro, bem como para gerar uma melhor compreensão dos eventos moleculares, bioquímicos e fisiológicos que modulam a formação do embrião. O metabolismo dos carboidratos é uma rota central que desempenha um papel importante durante o crescimento e desenvolvimento das plantas. Além de seu papel essencial como substrato no metabolismo de carbono e energia, os açúcares também desempenham papéis importantes como moléculas sinalizadoras. Para A. angustifolia, os bancos de dados de transcriptoma e proteoma identificaram o metabolismo de carboidratos como uma via importante na modulação do processo embriogênico. Assim, o objetivo principal deste trabalho foi estudar o metabolismo de carboidratos durante três estádios da embriogênese zigótica (globular, cotiledonar e maduro) e nas fases de proliferação e maturação de linhagens celulares embriogênicas com potencial embriogênico contrastante (responsiva e bloqueada). Para tanto, foram analisados os perfis de carboidratos não estruturais e monossacarídeos de parede celular, bem como a identificação e caracterização dos principais genes e proteínas envolvidos nas respostas mediadas por carboidratos, na homeostase de comunicação célula-a-célula e na modulação do metabolismo de sacarose, amido, rafinose e parede celular. Adicionalmente, um banco de dados de metaboloma foi gerado e integrado ao transcriptoma e proteoma de A. angustifolia através de redes de co-expressão, em uma abordagem de biologia de sistema. As respostas mediadas por carboidratos que ocorrem durante a embriogênese somática de A. angustifolia se assemelham às que ocorrem nos estádios iniciais da embriogênese zigótica. Além disso, o acúmulo de sacarose e amido durante o desenvolvimento embrionário foi modulado pelas respostas de detecção de açúcar e sinalização, destacando este processo como uma característica importante que direciona a responsividade das linhas celulares embriogênicas. Associado a isso, a seletividade mediada pela comunicação de plasmodesmas e transporte vesicular na linhagem celular responsiva, apareceu como importante controle de diferenciação e desenvolvimento de tipos celulares. Embora o mecanismo completo da embriogênese somática de A. angustifolia não tenha sido completamente elucidado, nossas análises sobre as alterações metabólicas (metaboloma) durante a embriogênese zigótica e somática indicam que as redes regulatórias envolvidas no crescimento e desenvolvimento estão altamente interconectadas aos níveis de metabólito, proteína e transcrito, mostrando altas correlações entre alvos envolvidos no metabolismo de carboidratos. Os resultados obtidos fornecem informações relevantes e inéditas sobre o metabolismo dos carboidratos na embriogênese zigótica e somática de A. angustifolia, bem como fornecem subsídios para a otimização das condições in vitro para o desenvolvimento de embriões somáticos. / Araucaria angustifolia is a native conifer species of Brazil, that with economic, social and ecological importance. Due to its intense exploitation, the species cover only 2% of its original forest area. In this system, somatic embryogenesis may be integrated into breeding and conservation programs. Beside this, the similarity between zygotic and somatic embryogenesis have been used to develop studies based on system biology, in order to optimize the in vitro somatic embryo development, as well as to generate a better understanding of molecular, biochemical and physiological events that modulate the embryogenesis. Carbohydrates metabolism is a central route that plays an important role during plant growth and development. In addition to its essential role as a substrate in carbon and energy metabolism, sugars also play important roles as signal molecules. For A. angustifolia, transcriptome and proteome databases identified carbohydrates metabolism as an important pathway in the modulation of embryogenic process. Thus, the main objective of this work was to study the carbohydrates metabolism during three zygotic embryogenesis stages (globular, cotyledonal and mature) and in proliferation and maturation phases of embryogenic cell lines with contrasting embryogenic potential (responsive and blocked). To achieve this purpose, the profiles of non-structural carbohydrates and cell wall monosaccharides were generated, as well as the identification and characterization of the main genes and proteins involved in carbohydrate-mediated responses, cell-to-cell communication homeostasis and modulation of sucrose, starch, raffinose and cell wall metabolism. Additionally, a metabolome database was generated and integrated with A. angustifolia transcriptome and proteome through co-expression networks in a system biology approach. The carbohydrate-mediated responses that occur during A. angustifolia somatic embryogenesis resembled those occurring in the early stages of zygotic embryogenesis, where the main responses that affect the targeting of the tissue differentiation of the seed occur. Beside this, sucrose and starch accumulation during embryo development were modulated by sugar sensing and signaling responses, highlighting this process as an important trait that directs the responsiveness of embryogenic cell lines. Associated to this, the selectivity mediated by plasmodesmata communication and vesicular transport in the responsive cell line, appeared as an important control of cell types differentiation and development. Even though the complete mechanism of A. angustifolia somatic embryogenesis has not been completely elucidated, our analyses about the metabolic changes (metabolome) during zygotic and somatic embryogenesis indicate that the regulatory networks involved in growth and development are highly inter-connected at the metabolite, protein and transcript levels, showing high correlations between targets involved in carbohydrate metabolism. The results obtained provide relevant and inedited information about the carbohydrates metabolism in A. angustifolia zygotic and somatic embryogenesis, as well as provide news subsidies for optimization of in vitro conditions for somatic embryos development.

Submitted by Maria Cristina (library@lncc.br) on 2017-05-04T13:17:54Z No. of bitstreams: 1 Tese - LNCC - Pablo Ivan Pereira Ramos.pdf: 25881844 bytes, checksum: 3737e5c6b0b1a08ca20ed86f209a96d7 (MD5) / Approved for entry into archive by Maria Cristina (library@lncc.br) on 2017-05-04T13:18:05Z (GMT) No. of bitstreams: 1 Tese - LNCC - Pablo Ivan Pereira Ramos.pdf: 25881844 bytes, checksum: 3737e5c6b0b1a08ca20ed86f209a96d7 (MD5) / Made available in DSpace on 2017-05-04T13:18:16Z (GMT). No. of bitstreams: 1 Tese - LNCC - Pablo Ivan Pereira Ramos.pdf: 25881844 bytes, checksum: 3737e5c6b0b1a08ca20ed86f209a96d7 (MD5) Previous issue date: 2016-06-30 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes) / The emergence of clinically important bacteria presenting a wide spectrum of antibiotic resistance represents a global concern. Although bacterial resistance was reported in the literature from the beggining of antibiotic use, early in the 20th century, we currently face the threat of pan-resistance, pathogens that can escape the action of all currently available antibiotic classes. A better understanding of virulence and resistance mechanisms, as well as new therapeutic options, are of paramount importance. This thesis proposal is based on the study of Klebsiella pneumoniae Kp13, a clinical isolate obtained in 2009 during a clonal outbreak in South Brazil which had its complete genome determined by our group. This strain is multidrug resistant and presents high-level resistance against polymyxin B (MIC 32 mg L􀀀1), a \last resort" drug for the treatment of Gram-negative multidrug-resistant bacteria. Using techniques from bioinformatics, transcriptomics (RNA-seq), systems biology and molecular modeling, we sought to better understand the gene expression response in K. pneumoniae in face of changes in abiotic characteristics and polymyxin B exposure. How these factors influence the metabolic repertoire of K. pneumoniae was also object of research. We also aimed to delineate a computational strategy for priorization of metabolic pathways that could serve as new targets for therapeuticals, by integrating expression, metabolic and structural reconstruction data. In parallel, this strategy was also applied to the study of Mycobacterium tubercluosis H37Rv (Mtb), the best characterized strain of this bacteria. E orts were made to study the metabolic complement of this pathogen, identifying important pathways related to its growth and correlating to molecular targets from a structural standview. The transcriptomic analyses allowed the identi cation of novel intracellular targets (such as ArcA-ArcB) that go beyond the \classic" e ect of polymyxin B mode of action, based in membrane interaction, besides drug-induced metabolic modulation which may lead to fermentative pathways of growth. The computational strategy for whole-genome target priorization led to the nding of pathways already known as druggable, such as S-methyl 5'-adenosin, as well as pathways not previously classi ed as druggable, but which could serve as candidates for future development of therapeutical compounds. / A emergência de isolados clínicos bacterianos apresentando resistência a uma ampla gama de medicamentos antibióticos representa uma preocupação global. Embora bactérias resistentes a alguns antibióticos já tenham sido relatadas na literatura médica desde o princípio do uso destas substâncias, no início do século XX, atualmente enfrentamos bactérias ditas panresistentes com capacidade de evadir à ação de todas as classes de drogas hoje disponíveis. O melhor entendimento dos mecanismos de resistência e virulência, bem como o delineamento de novas estratégias para o desenvolvimento de opções terapêuticas alternativas torna-se, portanto, imperativo. A presente proposta de tese de doutoramento tem como objeto de estudo central a bactéria Klebsiella pneumoniae Kp13, isolada no Sul do Brasil em 2009 na ocasião de um surto clonal e cujo genoma foi completamente determinado por nosso grupo. Esta cepa possui resistência multi-droga incluindo polimixina B (MIC > 32 mg L􀀀1), antibiótico considerado de último recurso no tratamento de patógenos Gram-negativos multi-resistentes. Utilizando técnicas de bioinformática, transcritômica (RNA-seq), biologia de sistemas e modelagem molecular, busca-se maior entendimento da resposta da ativação/desativação gênica de K. pneumonia frente a variações do meio e a exposição à polimixina B e como estes infuenciam no repertório metabólico exibido por esta bactéria. Ademais, objetiva-se delinear uma estratégia computacional para priorização de vias metabólicas servir como novos alvos terapêuticos para o controle deste importante patógeno, utilizando uma estratégia que integra os dados de expressão, metabólicos e da reconstrução estrutural. Em paralelo, esta estratégia foi também aplicada ao estudo de Mycobacterium tuberculosis H37Rv (Mtb), a cepa mais bem caracterizada desta bactéria. Foi dado um foco no complemento metabólico de Mtb, realizando a reconstrução de vias metabólicas importantes ao seu crescimento, correlacionando-as com alvos proteicos do ponto de vista estrutural. A análise transcritômica permitiu identificar possíveis alvos intracelulares que vão além do efeito “clássico" de ação da polimixina, baseado em interação com a membrana externa, tais como o sistema ArcA-ArcB, al_em de modulação metabólica induzida pelo fármaco, levando ao crescimento fermentativo da bactéria. A priorização de alvos moleculares permitiu identificar vias reconhecidamente drogáveis, tais como o metabolismo de S-metil 5'-adenosina, além de vias anteriormente não identificadas como drogáveis, mas que poderiam servir como candidatos para o desenvolvimento de novos fármacos.

INTRODUCTION: Breast cancer is the most common cancer in women worldwide and metastatic dissemination is the principal factor related to death by this disease. Breast cancer stem cells (bCSC), defined in this work as the ALDH1high/LIN-/ESA+ population, are thought to be responsible for metastasis and chemoresistance. The objective of this work is to find gene master regulators, in particular transcription factors (TFs), which are controlling the bCSC phenotype. METHODS: We used in this work two groups of datasets with transcriptome data, the discovery dataset group contains one dataset obtained by ourselves containing three paired samples comparing the bCSC and the bulk of the tumor (My Data - bCSC/Bulk dataset), a dataset with eight paired samples comparing the bCSC and cancer cells (Wicha - bCSC/CC dataset) and a dataset with 115 samples of breast cancer tissue (clinical response dataset). The second group, validation datasets, contains the BRCA-TCGA dataset with information of 621 samples, 4142 breast cancer samples of the Kmplot tool, 17 primary samples of BasL subtype and their information of grafting in patient derived xenografts and analyzes of cell lines (MF10A and HMLE). For the analyzes we used the paired t-test in the Limma R package, the ARACNE algorithm for the inference of regulons in the clinical response dataset, MRA-FET to define the master regulators of the bCSC phenotype, and GSEA to identify the biological meaning of the findings in the different datasets. RESULTS: We identified 12 TFs as master regulators of the bCSC phenotype, with nine of them forming two highly interconnected networks, one positively related with the bCSC phenotype formed by SNAI2, TWIST, PRRX1, BNC2 and TBX5 with its regulons, defined here as the mesenchymal transcription network and one negative correlated to the phenotype formed by SCML4, ZNF831, SP140 and IKZF3, defined as the immune response transcription network, totally unknown in the context of breast cancer in the literature. Although still with weak evidence, ZEB1 seems to control the two networks and can be responsible for the expression of ALDH1 and of the three remaining TFs: ID4, HOXA5 and TEAD1. As their names portray, our data showed in the different datasets, and independently of the molecular subtype and of the platform used, that the mesenchymal transcription network seems to be responsible for the bCSC phenotype and the immune response transcription network to the adaptive immune response in the tumor and a better prognosis for the patients. We also defined 10 membrane proteins as new markers and/or therapeutic targets of the bCSC. CONCLUSION: We found and described two TF networks that seem to control the bCSC phenotype, one of them totally unknown until now and correlated to a good prognosis. Our findings have a clear potential for clinical use. / INTRODUÇÃO: O cancer de mama é no mundo o câncer mais comum em mulheres e a disseminação metastática é o principal fator relacionado com a morte pela doença. Acreditasse que as células tronco do câncer de mama - bCSC, na sigla em inglês e definida neste trabalho com a população ALDH1high/LIN-/ESA+ - é responsável pela metástase e pela quimioresistência. O objetivo deste trabalho é encontrar genes que são essenciais para o controle do fenótipo das bCSC, em particular fatores de transcrição. MATERIAIS E MÉTODOS: Nesse trabalho nós utlizamos dois grupos de datasets com dados do transcriptoma, o grupo de datasets de descoberta contém um dataset gerado por nós com 3 amostras pareadas comparando as bCSC com o tumor total (My Data - bCSC/Bulk dataset), um dataset com 8 amostras pareadas comparando as bCSC com as células cancerígenas (Wicha - bCSC/CC dataset) e um dataset com 115 amostras de tecido de câncer de mama (Clinical Response dataset). O segundo grupo, grupo de validação, contém o dataset BRCA-TCGA com 621 amostras, as 4142 amostras de câncer de mama da ferramenta Kmplot, as 17 amostras humanas primárias do subtipo BasL e sua informação sobre a geração, ou não, de tumores em camundongos imunosuprimidos e a análise de linhagens celulares (MF10A e HMLE). Para a análise dos dataset utilizamos o test-t pareado no pacote Limma da liguagem R, o algoritmo ARACNE para a inferência de regulons no dataset Clinical Response, a análise MRA-FET para definir os Reguladores Mestres para o fenótipo das bCSC e a análise GSEA para identificar o significado biológico de nosso achados nos diferentes datasets. RESULTADOS E DISCUSSÃO: Nós identificamos 12 TFs como reguladores mestres, com 9 deles formando duas redes altamente conectadas, uma positivamente relacionada ao fenótipo bCSC formada por SNAI2, TWIST, PRRX1, BNC2 e TBX5 com seus regulons, e definida aqui como a rede de transcrição mesenquimal, e uma rede correlacionada negativamente, formada por SCML4, ZNF831, SP140 e IKZF3, definida aqui como a rede de transcrição da resposta imune e totalmente desconhecida da literatura no contexto do câncer de mama. Embora ainda com fraca evidencia, ZEB1 para controlar as duas redes e ser responsável pela expressão de ALDH1 e dos 3 TFs restantes: ID4, HOXA5 e TEAD1. Como mostram seus nomes, e independente do dataset, do subtipo molecular ou da plataforma utilizada, a rede de transcrição mesenquimal, parece ser responsável pela manutenção do fenótipo de células tronco cancerígenas e a rede de transcrição da resposta imune pela resposta imune adaptativa ao tumor e a um bom prognóstico para as pacientes. CONCLUSÃO: Nós encontramos e descrevemos duas redes de fatores de transcrição que parecem controlar o fenótipo das bCSC, uma delas totalmente desconhecida até agora e relacionada a um bom prognóstico. Nosso achados possuem um claro potencial para uso clínico.

Understanding the mechanisms of gene regulation in the field of systems biology is a very important issue. With the development of bio-information technology, we can capture large quantities of gene¡¦s expression data from DNA microarray data. In order to discover the relationship of gene regulation, the simulation of gene regulatory networks have been proposed. Among these simulations methods, the S-system model is the most widely used in non-linear differential equations. It can simulate the dynamic behavior of gene regulatory networks and gene expression, but can¡¦t explain the structure and orientation of gene regulatory networks. Therefore, we propose a Boolean knowledge-based approach to assist the S-system modeling of gene regulatory networks. In this study, we derive the positive and negative regulatory relationships between genes from the regulation of S-system parameters, and use the structure of Boolean networks as our knowledge base. According to the results of the experiment, we can verify our assumptions for the regulation of the S-system parameters, and also has a better understanding of the regulatory relationship between genes.