Systematic genetic analysis of transcription factors to map the fission yeast transcription-regulatory network

2013 ◽  
Vol 41 (6) ◽  
pp. 1696-1700 ◽  
Author(s):  
Gordon Chua
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Fission Yeast ◽  
Regulatory Networks ◽  
Target Genes ◽  
Transcriptome Profiling ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory ◽  
Transcription Factor Genes ◽  
Almost All

Mapping transcriptional-regulatory networks requires the identification of target genes, binding specificities and signalling pathways of transcription factors. However, the characterization of each transcription factor sufficiently for deciphering such networks remains laborious. The recent availability of overexpression and deletion strains for almost all of the transcription factor genes in the fission yeast Schizosaccharomyces pombe provides a valuable resource to better investigate transcription factors using systematic genetics. In the present paper, I review and discuss the utility of these strain collections combined with transcriptome profiling and genome-wide chromatin immunoprecipitation to identify the target genes of transcription factors.

scholarly journals Genome-wide binding analysis of the tomato transcription factor SlDof1 reveals its regulatory impacts on fruit ripening

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Yuying Wang ◽  
Peiwen Wang ◽  
Weihao Wang ◽  
Lingxi Kong ◽  
Shiping Tian ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Fruit Ripening ◽  
Regulatory Networks ◽  
Target Genes ◽  
Transcriptome Profiling ◽  
Flowering Plant ◽  
Transcriptional Regulatory Networks ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Chromatin Immunoprecipitation Sequencing

AbstractThe DNA binding with one finger (Dof) proteins are plant-specific transcription factors involved in a variety of biological processes. However, little is known about their functions in fruit ripening, a flowering-plant-specific process that is required for seed maturation and dispersal. Here, we found that the tomato Dof transcription factor SlDof1, is necessary for normal fruit ripening. Knockdown of SlDof1 expression by RNA interference delayed ripening-related processes, including lycopene synthesis and ethylene production. Transcriptome profiling indicated that SlDof1 influences the expression of hundreds of genes, and a chromatin immunoprecipitation sequencing revealed a large number of SlDof1 binding sites. A total of 312 genes were identified as direct targets of SlDof1, among which 162 were negatively regulated by SlDof1 and 150 were positively regulated. The SlDof1 target genes were involved in a variety of metabolic pathways, and follow-up analyses verified that SlDof1 directly regulates some well-known ripening-related genes including ACS2 and PG2A as well as transcriptional repressor genes such as SlIAA27. Our findings provide insights into the transcriptional regulatory networks underlying fruit ripening and highlight a gene potentially useful for genetic engineering to control ripening.

INFERRING THE REGULATORY INTERACTION MODELS OF TRANSCRIPTION FACTORS IN TRANSCRIPTIONAL REGULATORY NETWORKS

2012 ◽  
Vol 10 (05) ◽  
pp. 1250012 ◽  
Author(s):  
SHERINE AWAD ◽  
NICHOLAS PANCHY ◽  
SEE-KIONG NG ◽  
JIN CHEN
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Target Genes ◽  
Expression Data ◽  
Regulatory Interaction ◽  
Transcriptional Regulatory Networks ◽  
Interaction Models ◽  
Transcriptional Regulatory

Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the differential expression of a target gene in a TRN is challenging, especially when multiple TFs collaboratively participate in the transcriptional regulation. To unravel the roles of the TFs in the regulatory networks, we model the underlying regulatory interactions in terms of the TF–target interactions' directions (activation or repression) and their corresponding logical roles (necessary and/or sufficient). We design a set of constraints that relate gene expression patterns to regulatory interaction models, and develop TRIM (Transcriptional Regulatory Interaction Model Inference), a new hidden Markov model, to infer the models of TF–target interactions in large-scale TRNs of complex organisms. Besides, by training TRIM with wild-type time-series gene expression data, the activation timepoints of each regulatory module can be obtained. To demonstrate the advantages of TRIM, we applied it on yeast TRN to infer the TF–target interaction models for individual TFs as well as pairs of TFs in collaborative regulatory modules. By comparing with TF knockout and other gene expression data, we were able to show that the performance of TRIM is clearly higher than DREM (the best existing algorithm). In addition, on an individual Arabidopsis binding network, we showed that the target genes' expression correlations can be significantly improved by incorporating the TF–target regulatory interaction models inferred by TRIM into the expression data analysis, which may introduce new knowledge in transcriptional dynamics and bioactivation.

Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors

2015 ◽  
Vol 7 (5) ◽  
pp. 560-568 ◽  
Author(s):  
Tobias Österlund ◽  
Sergio Bordel ◽  
Jens Nielsen
Keyword(s):  
Transcriptional Regulation ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Target Genes ◽  
Living Cells ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory ◽  
Control Patterns

Transcriptional regulation is the most committed type of regulation in living cells where transcription factors (TFs) control the expression of their target genes and TF expression is controlled by other TFs forming complex transcriptional regulatory networks that can be highly interconnected.

Transcriptional Control of Parturition: Insights from Gene Regulation Studies in the Myometrium

2021 ◽  
Author(s):  
Nawrah Khader ◽  
Virlana M Shchuka ◽  
Oksana Shynlova ◽  
Jennifer A Mitchell
Keyword(s):  
Transcription Factors ◽  
Regulatory Networks ◽  
Transcriptional Control ◽  
Progesterone Receptors ◽  
Activator Protein 1 ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Pathways ◽  
Transcriptional Regulatory ◽  
Mechanistic Basis ◽  
Labour Onset

Abstract The onset of labour is a culmination of a series of highly coordinated and preparatory physiological events that take place throughout the gestational period. In order to produce the associated contractions needed for fetal delivery, smooth muscle cells in the muscular layer of the uterus (i.e. myometrium) undergo a transition from quiescent to contractile phenotypes. Here, we present the current understanding of the roles transcription factors play in critical labour-associated gene expression changes as part of the molecular mechanistic basis for this transition. Consideration is given to both transcription factors that have been well-studied in a myometrial context, i.e. activator protein 1 (AP-1), progesterone receptors (PRs), estrogen receptors (ERs), and nuclear factor kappa B (NF-κB), as well as additional transcription factors whose gestational event-driving contributions have been demonstrated more recently. These transcription factors may form pregnancy- and labour- associated transcriptional regulatory networks in the myometrium to modulate the timing of labour onset. A more thorough understanding of the transcription factor-mediated, labour-promoting regulatory pathways holds promise for the development of new therapeutic treatments that can be used for the prevention of preterm labour in at-risk women.

scholarly journals Dissecting the Repertoire of DNA-Binding Transcription Factors of the Archaeon Pyrococcus furiosus DSM 3638

Life ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 40 ◽  
Author(s):  
Antonia Denis ◽  
Mario Alberto Martínez-Núñez ◽  
Silvia Tenorio-Salgado ◽  
Ernesto Perez-Rueda
Keyword(s):  
Transcription Factors ◽  
Dna Binding ◽  
Regulatory Networks ◽  
Pyrococcus Furiosus ◽  
Structural Domain ◽  
Transcriptional Regulatory Networks ◽  
Dna Binding Domains ◽  
Binding Domains ◽  
Transcriptional Regulatory ◽  
Cellular Domain

In recent years, there has been a large increase in the amount of experimental evidence for diverse archaeal organisms, and these findings allow for a comprehensive analysis of archaeal genetic organization. However, studies about regulatory mechanisms in this cellular domain are still limited. In this context, we identified a repertoire of 86 DNA-binding transcription factors (TFs) in the archaeon Pyrococcus furiosus DSM 3638, that are clustered into 32 evolutionary families. In structural terms, 45% of these proteins are composed of one structural domain, 41% have two domains, and 14% have three structural domains. The most abundant DNA-binding domain corresponds to the winged helix-turn-helix domain; with few alternative DNA-binding domains. We also identified seven regulons, which represent 13.5% (279 genes) of the total genes in this archaeon. These analyses increase our knowledge about gene regulation in P. furiosus DSM 3638 and provide additional clues for comprehensive modeling of transcriptional regulatory networks in the Archaea cellular domain.

scholarly journals The PARP Inhibitor Olaparib Modulates the Transcriptional Regulatory Networks of Long Non-Coding RNAs during Vasculogenic Mimicry

Cells ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2690
Author(s):  
Mónica Fernández-Cortés ◽  
Eduardo Andrés-León ◽  
Francisco Javier Oliver
Keyword(s):  
Transcription Factors ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Target Genes ◽  
Parp Inhibitor ◽  
Vasculogenic Mimicry ◽  
Transcriptome Profiling ◽  
Key Species ◽  
Non Coding Rnas ◽  
The Impact

In highly metastatic tumors, vasculogenic mimicry (VM) involves the acquisition by tumor cells of endothelial-like traits. Poly-(ADP-ribose) polymerase (PARP) inhibitors are currently used against tumors displaying BRCA1/2-dependent deficient homologous recombination, and they may have antimetastatic activity. Long non-coding RNAs (lncRNAs) are emerging as key species-specific regulators of cellular and disease processes. To evaluate the impact of olaparib treatment in the context of non-coding RNA, we have analyzed the expression of lncRNA after performing unbiased whole-transcriptome profiling of human uveal melanoma cells cultured to form VM. RNAseq revealed that the non-coding transcriptomic landscape differed between olaparib-treated and non-treated cells: olaparib significantly modulated the expression of 20 lncRNAs, 11 lncRNAs being upregulated, and 9 downregulated. We subjected the data to different bioinformatics tools and analysis in public databases. We found that copy-number variation alterations in some olaparib-modulated lncRNAs had a statistically significant correlation with alterations in some key tumor suppressor genes. Furthermore, the lncRNAs that were modulated by olaparib appeared to be regulated by common transcription factors: ETS1 had high-score binding sites in the promoters of all olaparib upregulated lncRNAs, while MZF1, RHOXF1 and NR2C2 had high-score binding sites in the promoters of all olaparib downregulated lncRNAs. Finally, we predicted that olaparib-modulated lncRNAs could further regulate several transcription factors and their subsequent target genes in melanoma, suggesting that olaparib may trigger a major shift in gene expression mediated by the regulation lncRNA. Globally, olaparib changed the lncRNA expression landscape during VM affecting angiogenesis-related genes.

scholarly journals Experiment level curation identifies high confidence transcriptional regulatory interactions in neurodevelopment

2021 ◽  
Author(s):  
Eric Ching-Pan Chu ◽  
Alexander Morin ◽  
Tak Hou Calvin Chang ◽  
Tue Nguyen ◽  
Yi-Cheng Tsai ◽  
...  
Keyword(s):  
Nervous System ◽  
Experimental Evidence ◽  
Regulatory Networks ◽  
Large Scale ◽  
Target Genes ◽  
High Confidence ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Interactions ◽  
Transcriptional Regulatory ◽  
Significant Enrichment

To facilitate the development of large-scale transcriptional regulatory networks (TRNs) that may enable in-silico analyses of disease mechanisms, a reliable catalogue of experimentally verified direct transcriptional regulatory interactions (DTRIs) is needed for training and validation. There has been a long history of using low-throughput experiments to validate single DTRIs. Therefore, we hypothesize that a reliable set of DTRIs could be produced by curating the published literature for such evidence. In our survey of previous curation efforts, we identified the lack of details about the quantity and the types of experimental evidence to be a major gap, despite the importance of such details for the identification of bona fide DTRIs. We developed a curation protocol to inspect the published literature for support of DTRIs at the experiment level, focusing on genes important to the development of the mammalian nervous system. We sought to record three types of low-throughput experiments: Transcription factor (TF) perturbation, TF-DNA binding, and TF-reporter assays. Using this protocol, we examined a total of 1,310 papers to assemble a collection of 1,499 unique DTRIs, involving 251 TFs and 825 target genes, many of which were not reported in any other DTRI resource. The majority of DTRIs (965, 64%) were supported by two or more types of experimental evidence and 27% were supported by all three. Of the DTRIs with all three types of evidence, 170 had been tested using primary tissues or cells and 44 had been tested directly in the central nervous system. We used our resource to document research biases among reports towards a small number of well-studied TFs. To demonstrate a use case for this resource, we compared our curation to a previously published high-throughput perturbation screen and found significant enrichment of the curated targets among genes differentially expressed in the developing brain in response to Pax6 deletion. This study demonstrates a proof-of-concept for the assembly of a high confidence DTRI resource in order to support the development of large-scale TRNs.

scholarly journals FLI1 and FRA1 transcription factors drive the transcriptional regulatory networks characterizing muscle invasive bladder cancer

2021 ◽  
Author(s):  
Perihan Yagmur Guneri Sozeri ◽  
Gulden Ozden Yilmaz ◽  
Asli Kisim ◽  
Aleyna Eray ◽  
Hamdiye Uzuner ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Transcription Factors ◽  
Regulatory Networks ◽  
Target Genes ◽  
Cell Junction ◽  
Transcriptional Regulatory Networks ◽  
Histopathological Classification ◽  
Knock Down ◽  
Muscle Invasive ◽  
Regulatory Landscape

Bladder cancer is mostly present in the form of urothelium carcinoma, causing over 150.000 deaths each year. Its histopathological classification as muscle invasive (MIBC) and non-muscle invasive (NMIBC) is the most prominent aspect, affecting the prognosis and progression of this disease. In this study, we defined the active regulatory landscape of MIBC and NMIBC cell lines using H3K27ac-seq and used an integrative data approach to combine our findings with existing data. Our analysis revealed FRA1 and FLI1 as the two critical transcription factors differentially regulating MIBC regulatory landscape. Importantly, we show that FRA1 and FLI1 regulate the genes involved in epithelial cell migration and cell junction organization. Knock-down of FRA1 and FLI1 in MIBC revealed the downregulation of several EMT-related genes such as MAP4K4 and FLOT1. Further, ChIP-SICAP performed for FRA1 and FLI1 enabled us to infer chromatin binding partners of these two transcription factors and link this information with their target genes, providing a comprehensive regulatory circuit for the genes implicated in invasive ability of the bladder cancer cells. Finally, for the first time we show that knock-down of FRA1 and FRA1-FLI1 double knock-down results in significant reduction of invasion capacity of MIBC cells towards muscle microenvironment using IC-CHIP assays. Our results collectively highlight the role of these two transcription factors in invasive characteristics of bladder cancer in selection and design of targeted options for treatment of MIBC.

scholarly journals The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat

2020 ◽  
Vol 10 (10) ◽  
pp. 3675-3686 ◽  
Author(s):  
Sophie A. Harrington ◽  
Anna E. Backhaus ◽  
Ajit Singh ◽  
Keywan Hassani-Pak ◽  
Cristobal Uauy
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Candidate Gene ◽  
Web Application ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Prediction ◽  
Relevant Information ◽  
Rna Seq ◽  
Biologically Relevant

Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate gene discovery. However, the extent to which the network predictions are biologically relevant is often unclear. Recently a GENIE3 network which predicted targets of wheat transcription factors was produced. Here we used an independent RNA-Seq dataset to test the predictions of the wheat GENIE3 network for the senescence-regulating transcription factor NAM-A1 (TraesCS6A02G108300). We re-analyzed the RNA-Seq data against the RefSeqv1.0 genome and identified a set of differentially expressed genes (DEGs) between the wild-type and nam-a1 mutant which recapitulated the known role of NAM-A1 in senescence and nutrient remobilisation. We found that the GENIE3-predicted target genes of NAM-A1 overlap significantly with the DEGs, more than would be expected by chance. Based on high levels of overlap between GENIE3-predicted target genes and the DEGs, we identified candidate senescence regulators. We then explored genome-wide trends in the network related to polyploidy and found that only homeologous transcription factors are likely to share predicted targets in common. However, homeologs which vary in expression levels across tissues are less likely to share predicted targets than those that do not, suggesting that they may be more likely to act in distinct pathways. This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions of transcription factor targets, which can be used for candidate gene prediction and for global analyses of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner web application, facilitating its use in future studies.

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