Dataset Information

Construction and analysis of dynamic transcription factor regulatory networks in the progression of glioma.

ABSTRACT: The combinatorial cross-regulation of transcription factors (TFs) plays an important role in cellular identity and function; however, the dynamic regulation of TFs during glioma progression remains largely unknown. Here, we used the genome-wide expression of TFs to construct an extensive human TF network comprising interactions among 513 TFs and to analyse the dynamics of the TF-TF network during glioma progression. We found that the TF regulatory networks share a common architecture and that the topological structures are conserved. Strikingly, despite the conservation of the network architecture, TF regulatory networks are highly grade specific, and TF circuitry motifs are dynamically rewired during glioma progression. In addition, the most frequently observed structure in the grade-specific TF networks was the feedforward loop (FFL). We described applications that show how investigating the behaviour of FFLs in glioblastoma can reveal FFLs (such as RARG-NR1I2-CDX2) that are associated with prognosis. We constructed comprehensive TF-TF networks and systematically analysed the circuitry, dynamics, and topological principles of the networks during glioma progression, which will further enhance our understanding of the functions of TFs in glioma.

SUBMITTER: Li Y

PROVIDER: S-EPMC4630656 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Construction and analysis of dynamic transcription factor regulatory networks in the progression of glioma.

Li Yongsheng Y Shao Tingting T Jiang Chunjie C Bai Jing J Wang Zishan Z Zhang Jinwen J Zhang Lili L Zhao Zheng Z Xu Juan J Li Xia X

Scientific reports 20151103

The combinatorial cross-regulation of transcription factors (TFs) plays an important role in cellular identity and function; however, the dynamic regulation of TFs during glioma progression remains largely unknown. Here, we used the genome-wide expression of TFs to construct an extensive human TF network comprising interactions among 513 TFs and to analyse the dynamics of the TF-TF network during glioma progression. We found that the TF regulatory networks share a common architecture and that th ...[more]

PMID: 26526635

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Project description:Gene regulatory networks (GRNs) are critical for dynamic transcriptional responses to environmental stress. However, the mechanisms by which GRN regulation adjusts physiology to enable stress survival remain unclear. Here we investigate the functions of transcription factors (TFs) within the global GRN of the stress-tolerant archaeal microorganism Halobacterium salinarum. We measured growth phenotypes of a panel of TF deletion mutants in high temporal resolution under heat shock, oxidative stress, and low-salinity conditions. To quantitate the noncanonical functional forms of the growth trajectories observed for these mutants, we developed a novel modeling framework based on Gaussian process regression and functional analysis of variance (FANOVA). We employ unique statistical tests to determine the significance of differential growth relative to the growth of the control strain. This analysis recapitulated known TF functions, revealed novel functions, and identified surprising secondary functions for characterized TFs. Strikingly, we observed that the majority of the TFs studied were required for growth under multiple stress conditions, pinpointing regulatory connections between the conditions tested. Correlations between quantitative phenotype trajectories of mutants are predictive of TF-TF connections within the GRN. These phenotypes are strongly concordant with predictions from statistical GRN models inferred from gene expression data alone. With genome-wide and targeted data sets, we provide detailed functional validation of novel TFs required for extreme oxidative stress and heat shock survival. Together, results presented in this study suggest that many TFs function under multiple conditions, thereby revealing high interconnectivity within the GRN and identifying the specific TFs required for communication between networks responding to disparate stressors. IMPORTANCE To ensure survival in the face of stress, microorganisms employ inducible damage repair pathways regulated by extensive and complex gene networks. Many archaea, microorganisms of the third domain of life, persist under extremes of temperature, salinity, and pH and under other conditions. In order to understand the cause-effect relationships between the dynamic function of the stress network and ultimate physiological consequences, this study characterized the physiological role of nearly one-third of all regulatory proteins known as transcription factors (TFs) in an archaeal organism. Using a unique quantitative phenotyping approach, we discovered functions for many novel TFs and revealed important secondary functions for known TFs. Surprisingly, many TFs are required for resisting multiple stressors, suggesting cross-regulation of stress responses. Through extensive validation experiments, we map the physiological roles of these novel TFs in stress response back to their position in the regulatory network wiring. This study advances understanding of the mechanisms underlying how microorganisms resist extreme stress. Given the generality of the methods employed, we expect that this study will enable future studies on how regulatory networks adjust cellular physiology in a diversity of organisms.

Project description:OBJECTIVE:Colorectal cancer (CRC) is an extremely common gastrointestinal malignancy. The present study aimed to identify microRNAs (miRNAs) and transcription factors (TFs) associated with tumor development. METHODS:Three miRNA profile datasets were integrated and analyzed to elucidate the potential key candidate miRNAs in CRC. The starBase database was used to identify the potential targets of common differentially expressed miRNAs (DEMs). Transcriptional Regulatory Element Database and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text databases were used to identify cancer-related TFs and the TF-regulated target genes. Functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integration Discovery (DAVID) database, and the miRNA-TF-gene networks were constructed by Cytoscape. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to detect the expression of genes and miRNAs. RESULTS:In total, 14 DEMs were found in CRC. By bioinformatics analysis, 5 DEMs (miR-145, miR-497, miR-30a, miR-31, and miR-20a) and 8 TFs (ELK4 (ETS-family transcription factor), myeloblastosis proto-oncogene like (MYBL)1, MYBL2, CEBPA, PPARA, PPARD, PPARG, and endothelial PAS domain protein (EPAS1)) appeared to be associated with CRC and were therefore used to construct miRNA-TF-gene networks. From the networks, we found that miR-20a might play the most important role as an miRNA in the networks. By qRT-PCR, we demonstrated that miR-20a was significantly upregulated in CRC tissues. We also performed qRT-PCR to identify the expression of miR-20a-related TFs (PPARA, PPARD, PPARG, EPAS1). Three of them, PPARA, PPARG, and EPAS1, were downregulated in CRC tissues, with statistically significant differences, while the downregulation of PPARD in CRC tissues was not significantly different. Pathway enrichment analyses indicated that the phosphoinositide 3-kinase (PI3K)-Akt signaling pathway was the most significantly enriched pathway. Two main elements of the PI3K-Akt signaling pathway, phosphatase and tensin homolog deleted on chromosome 10 and B-cell lymphoma 2-associated agonist of cell death, were demonstrated to be downregulated in CRC. CONCLUSION:The present study identified hub miRNAs and miRNA-related TF regulatory networks in CRC, which might be potential targets for the diagnosis and treatment of CRC.

Project description: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.

Dataset Information

Construction and analysis of dynamic transcription factor regulatory networks in the progression of glioma.

Publications

Construction and analysis of dynamic transcription factor regulatory networks in the progression of glioma.

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