A novel statistical approach for identification of the master regulator transcription factor

Test Dataset. This file contains an example test dataset where our method can be implemented. This simulated data contains 10 transcription factors, namely TF 1, TF 2, …, TF 10 along with 105 genes that were regulated by these transcription factors. Among the transcription factors, TF 1 was generated to play the role of the master regulator. (CSV 1382 kb

Additional file 2: of Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

GO term enrichment for expression-based clusters in rapamycin-treated yeast. (XLSX 161 kb

Additional file 5: of Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

Lists of enriched GO terms for the direct protein interactors of subsets of driver TFs that are ranked higher using the combined network score than either individual network. Lists correspond to the following conditions: rapamycin, diamide and menadione in yeast, and viral oncogene perturbation in humans. (XLSX 77 kb

Additional file 4: of Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

Top transcription factors, as ranked either by 1) differential expression by transforming viral oncogenes, 2) degree in the GMIT network or 3) degree in the PANDA network. (XLSX 41 kb

Additional file 1: Supplementary Text and Figures. of Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

Supplementary text describes (a) the robustness of the results to the growth rate cutoff for yeast strains, (b) an analysis combining differential expression with protein interaction degree, and (c) the characterization of the protein interactors of driver TFs. Figures present results for (i) network integration for rapamycin data with alternative growth cutoff, (ii) PANDA analysis of rapamycin expression data, (iii) rapamycin network integration using only yeast two-hybrid data, (iv) results of combining differential expression with PPI degree, (v) ROC curves for network integration in multiple yeast conditions, and (vi) the rank characteristics and local PPI neighborhoods of driver TFs for menadione, DTT and diamide. (PDF 1304 kb

Identificação de reguladores mestres em adenocarcinoma de pulmão e sua utilização para a prospecção de compostos antitumorais

O câncer de pulmão é uma das neoplasias malignas mais incidentes e letais da oncologia. Ademais, o adenocarcinoma pulmonar compreende o subtipo histológico mais comum e cuja frequência tem aumentado em detrimento de outros tipos nos últimos anos, especialmente em mulheres. Portanto, o entendimento da patofisiologia deste tipo de câncer e a busca por biomarcadores confiáveis, além de novas abordagens terapêuticas e regimes de tratamento, constituem áreas importantes de pesquisa e avanço biomédico. Nas últimas décadas, a Biologia de Sistemas coalesceu e fortaleceu-se com o advento de tecnologias ômicas e da bioinformática, viabilizando e impulsionando o estudo da biologia no contexto de sistemas complexos. Desta forma, este trabalho procura utilizar dados transcriptômicos e estratégias de bioinformática para obter fatores de transcrição candidatos a reguladores mestre do adenocarcinoma pulmonar, utilizado métodos, conceitos e visões oriundas da Biologia de Sistemas. Adicionalmente, desenvolvemos uma metodologia de reposicionamento computacional de drogas e aplicamos esta estratégia para obter drogas candidatas a elaboração de novos regimes terapêuticos. O primeiro passo do estudo foi a reconstrução de redes de co-expressão gênica centradas em fatores de transcrição e seus alvos utilizando informação de tecido não-tumoral, a fim de estabelecer redes de referência. Posteriormente, os grupos de genes constituídos pelos fatores de transcrição e seus alvos, conjuntamente chamados de unidades regulatórias, foram investigados quanto a seus perfis de expressão diferencial utilizando estudos caso-controle. As unidades regulatórias dos fatores de transcrição enriquecidos de genes diferencialmente expressos em mais de 80% dos estudos caso-controle, para ambas as redes de referência, foram consideradas reguladores mestre candidatos da patologia. Esta estratégia resultou em nove fatores de transcrição – ATOH8, DACH1, EPAS1, ETV5, FOXA2, FOXM1, HOXA4, SMAD6 e UHRF1. Em seguida, testamos se os estados de ativação inferidos para estes fatores de transcrição possuíam potencial prognóstico em diferentes coortes de adenocarcinoma, e observamos que três dos nove mostraram associações consistentes com o desfecho de pacientes. Finalmente, utilizamos as unidades regulatórias destes três fatores de transcrição – FOXA2, FOXM1 e UHRF1 – para prospectar drogas candidatas a reposicionamento, o que resultou em seis compostos potencialmente capazes de reverter os perfis transcricionais encontrados no contexto patológico. Estes compostos são: deptropina, promazina, ácido valproico, azaciclonol, metotrexato e composto ChemBridge ID 5109870. Avaliações dos potenciais terapêuticos destes fármacos e seus mecanismos de ação neste câncer podem auxiliar no desenvolvimento de novos tratamentos. Da mesma forma, elucidação dos papéis biológicos específicos dos nove reguladores mestres também tem grande potencial de contribuir para o entendimento da biologia do adenocarcinoma de pulmão.Lung cancer is one of the most common and lethal pathologies of medical oncology. Furthermore, adenocarcinoma comprises the most prevalent lung cancer histological subtype, which frequency increased over other types in recent years, especially among women. For these reasons, further understanding about the pathophysiology of this type of cancer and the search for reliable biomarkers, for new therapeutic drugs and for improved treatment strategies are all important areas of biomedical research and development. In recent decades, the Systems Biology paradigm emerged and strengthened due to novel omic technologies and bioinformatics, enabling and enhancing the study of biological phenomena in the context of complex systems. Thus, this study aims to search for the transcription factors acting as master regulators of lung adenocarcinoma using transcriptomics and employing Systems Biology concepts and views. Additionally, we developed a computational drug repositioning method and implemented it to retrieve candidate molecules for new treatment strategies. The first step in our study involved the reconstruction of co-expression gene networks centered in transcription factors and their targets using non-tumoral data in order to establish reference networks. Afterwards, the groups of genes comprising transcription factors and their targets, collectively called regulatory units, were queried for their differential expression profiles using case-control studies. Regulatory units of the transcription factors enriched with differentially expressed genes in over 80% of case-control studies, for both reference networks, were considered master regulator candidates of the disease. This strategy retrieved nine transcription factors - ATOH8, DACH1, EPAS1, ETV5, FOXA2, FOXM1, HOXA4, SMAD6 and UHRF1. Following that, we tested whether the inferred activities of these master regulators' regulatory units were associated with patient survival using several cohorts datasets, which highlighted three of them consistently associated with patient outcome. Finally, the regulatory units of these three transcription factors - FOXA2, FOXM1 e UHRF1 - were used to query drug candidates for repositioning in lung adenocarcinoma, resulting in six molecules capable to revert disease's the transcriptional profile. These drugs were deptropine, promazine, valproic acid, azacyclonol, methotrexate and ChemBridge ID compound 5109870. The evaluation of their therapeutic potentials and mechanisms of action in lung cancer may assist the development of new treatments. Additionally, further investigations of the retrieved master regulators' roles may lead to improvements in our understanding of adenocarcinoma pathophysiology

Λειτουργική ανάλυση του γονιδίου FRA10AC1 του ανθρώπου στο πλαίσιο του δικτύου πρωτεϊνικών αλληλεπιδράσεων του σωματίου συναρμογής

Σημείωση: διατίθεται συμπληρωματικό υλικό σε ξεχωριστό αρχείο