Inferring microRNA and transcription factor regulatory networks in heterogeneous data

Background: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. Results: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network. We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT. Conclusions: We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.Thuc D Le, Lin Liu, Bing Liu, Anna Tsykin, Gregory J Goodall, Kenji Satou and Jiuyong L

RTNduals : ferramenta para análise de co-regulação entre regulons e inferência de dual regulons

Orientador : Prof. Dr. Mauro Antonio Alves CastroCoorientador : Prof. Dr. Rodrigo AlmeidaDissertação (mestrado) - Universidade Federal do Paraná, Setor de Educação Profissional e Tecnológica, Programa de Pós-Graduação em Bioinformática. Defesa: Curitiba, 14/06/2017Inclui referências : f. 48-50Resumo: A regulação de expressão gênica é um fator chave nos processos biológicos e composta de uma extensa cascata de eventos que envolvem diversas moléculas e elementos. Entre estes elementos podemos destacar importantes reguladores tais como fatores de transcrição (FTs) e microRNAs (miRNAs), os quais podem ativar ou reprimir seus genes alvo e ainda cooperar ou competir em redes regulatórias. A complexidade dos efeitos destes reguladores requer o desenvolvimento de novos modelos capazes de integrar informação regulatória proveniente de diferentes tipos de dados. Embora exista uma variedade de ferramentas para o estudo de redes regulatórias, ainda existem deficiências metodológicas para integrar co-regulação entre reguladores e avaliar o efeito desta co-regulação em seus genes alvo. Neste trabalho nós apresentamos o software RTNduals, um pacote R/Bioconductor capaz de identificar dual regulons, um novo conceito que descreve pares de regulons cujos alvos em comum são afetados pelos dois reguladores. O pacote é uma extensão do software RTN (Reconstruction of Transcriptional Networks) e utiliza redes transcricionais para computar alvos compartilhados por dois reguladores e avaliar o efeito de ambos sobre os alvos compartilhados. O pacote RTNduals permite determinar se dois reguladores tem efeito similar ou oposto no conjunto de alvos compartilhados. Palavras-chaves: Redes Regulatórias; Regulons; Co-regulação; Fatores de transcrição; microRNAs.Abstract: The regulation of gene expression is a key factor in biological processes and it is composed of an extensive cascade of events involving several molecules and elements. We can highlight important regulators such as Transcription Factors (FTs) and microRNAs (miRNAs), which can activate or repress their target genes and still cooperate or compete in regulatory networks. The complexity of the effects among these regulators requires the development of new models capable of integrating regulatory information from different types of data. Although there are a variety of tools for the study of regulatory networks, there is still a methodological gap to integrate co-regulation between regulators and to evaluate the effect of this co-regulation on their target genes. In this work we present the RTNduals, an R/Bioconductor package capable of identifying dual regulons, which represent pairs of regulons whose common targets are affected by both regulators. The package is an extension of the Reconstruction of Transcriptional Networks (RTN) software and it uses RTN-generated transcriptional networks to test when pairs of regulators have a similar effect on their set of shared targets genes. With the set, RTNduals allows to determine whether two regulators have similar or opposite effect on their shared targets. Key-words: Regulatory networks; Regulons; Co-regulation; Transcription Factors; microRNAs

CyTRANSFINDER: a Cytoscape 3.3 plugin for three-component (TF, gene, miRNA) signal transduction pathway construction

Background: Biological research increasingly relies on network models to study complex phenomena. Signal Transduction Pathways are molecular circuits that model how cells receive, process, and respond to information from the environment providing snapshots of the overall cell dynamics. Most of the attempts to reconstruct signal transduction pathways are limited to single regulator networks including only genes/proteins. However, networks involving a single type of regulator and neglecting transcriptional and post-transcriptional regulations mediated by transcription factors and microRNAs, respectively, may not fully reveal the complex regulatory mechanisms of a cell. We observed a lack of computational instruments supporting explorative analysis on this type of three-component signal transduction pathways. Results: We have developed CyTRANSFINDER, a new Cytoscape plugin able to infer three-component signal transduction pathways based on user defined regulatory patterns and including miRNAs, TFs and genes. Since CyTRANSFINDER has been designed to support exploratory analysis, it does not rely on expression data. To show the potential of the plugin we have applied it in a study of two miRNAs that are particularly relevant in human melanoma progression, miR-146a and miR-214. Conclusions: CyTRANSFINDERsupportsthereconstructionofsmallsignaltransductionpathwaysamonggroupsof genes. Results obtained from its use in a real case study have been analyzed and validated through both literature data and preliminary wet-lab experiments, showing the potential of this tool when performing exploratory analysi

Decipher Mechanisms by which Nuclear Respiratory Factor One (NRF1) Coordinates Changes in the Transcriptional and Chromatin Landscape Affecting Development and Progression of Invasive Breast Cancer

Despite tremendous progress in the understanding of breast cancer (BC), gaps remain in our knowledge of the molecular basis underlying the aggressiveness of BC and BC disparities. Nuclear respiratory factor 1 (NRF1) is a transcription factor (TF) known to control breast cancer cell cycle progression. DNA response elements bound by NRF1 positively correlate with the progression of malignant breast cancer. Mechanistic aspects by which NRF1 contributes to susceptibility to different breast tumor subtypes are still not fully understood. Therefore, the primary objective of this dissertation was to decipher mechanisms by which NRF1 coordinates changes in the transcriptional and chromatin landscape affecting development and progression of invasive breast cancer. Our hypothesis was that NRF1 reprogramming the transcription of tumor initiating gene(s) and tumor suppressor gene(s) contribute in the development and progression of invasive breast cancer. To test this hypothesis, we proposed three specific aims: (a) Decipher regulatory landscape of NRF1 networks in breast cancer. (b) Determine the role of NRF1 gene networks in different subtypes of breast cancer. (c) Determine differential NRF1 gene network sensitivity contributing to breast cancer disparities. Our approach to test these aims consisted of a systematic integration of ChIP DNA-seq, RNA-Seq, NRF1 protein-DNA motif binding, signal pathway analysis, and Bayesian machine learning. We uncovered a novel oncogenic role for NRF1. This discovery strongly supported the supposition that NRF1 overexpression is sufficient to derive breast tumorigenesis. We also observed new roles for NRF1 in the acquisition of breast tumor initiating cells, regulation of epithelial to mesenchymal transition (EMT), and invasiveness of BC stem cells. Furthermore, through the use of Bayesian network structure learning we found that the NRF1 motif was enriched in 14 associated with HER2 amplified breast cancer. Three genes—GSK3B, E2F3, and PIK3CA—were able to predict HER2 breast tumor status with 96% to100% confidence. The findings of this study also showed the roles of NRF1 sensitivity to development of lobular A, Her2+, and TNBC in different racial/ethnic groups of breast cancer patients. In summary, our study revealed for the first time the role of NRF1 in the pathogenesis of invasive BC and BC disparities

Systemic Modeling of Biomolecular Interaction Networks

For more than the entire past century, classical experimental methodologies have dominated biological research, providing a wealth of information about individual molecular species in cells and their functions. However, there is an increasing and strong level of evidence suggesting that an isolated biological function can only rarely be attributed to an individual biological molecule. Instead, more recently, it is argued that most biological characteristics are due to complex interactions between the cell’s numerous constituents, such as proteins, DNA and RNA. Therefore, a major challenge for the biological sciences in this century is to unravel the structure and the dynamics of these complex intracellular interactions at a systems level.   Many types of statistical and computational models have been built and applied to study cellular behavior and in this research work, we focus on two distinct instances, one from each of the two broad types of models used in computational systems biology: i) statistical inference models applied to gene regulatory interaction networks and ii) biochemical reaction models applied to protein-protein interaction networks.   For our first research problem, we focused on microRNA-mediated gene regulatory networks. MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) that extensively regulate gene expression in metazoan animals, plants and protozoa. With the goal to gain a systemic understanding of miRNA-mediated interaction networks, we developed IntegraMiR, a novel integrative analysis method that can be used to infer certain types of regulatory loops of dysregulated miRNA/Transcription Factor (TF) interactions which appear at the transcriptional, post-transcriptional and signaling levels in a statistically over-represented manner. We demonstrate instances of the results in a number of distinct biological settings, which are known to play crucial roles in the contexts of prostate cancer and autism spectrum disorders.   To study the dynamics of biomolecular interaction networks, we focused on a protein-protein interaction network in living cells. Our collaborators at the School of Medicine planned to synthetically develop and characterize a biomaterial, which was produced by this protein-protein interaction network, and which would act as a molecular sieve to control the passage of biomolecules in living cells. And we wanted to computationally model the dynamic formation of this biomolecular sieve, termed a hydrogel, and characterize its properties that were relevant to the experimental work. The resulting model presented us and our experimental collaborators with a systemic and deeper understanding of the problem of gel synthesis, which guided the experimental design and provided further validation of the subsequent experimental findings and conclusions