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.

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 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 RNA-seq and ChIP-seq as Complementary Approaches for Comprehension of Plant Transcriptional Regulatory Mechanism

2019 ◽  
Vol 21 (1) ◽  
pp. 167 ◽  
Author(s):  
Isiaka Ibrahim Muhammad ◽  
Sze Ling Kong ◽  
Siti Nor Akmar Abdullah ◽  
Umaiyal Munusamy
Keyword(s):  
Transcriptional Regulation ◽  
Regulatory Networks ◽  
Data Sets ◽  
Rna Seq ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Pathways ◽  
Specific Biological Process ◽  
Transcriptional Regulatory ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Sequencing Platforms

The availability of data produced from various sequencing platforms offer the possibility to answer complex questions in plant research. However, drawbacks can arise when there are gaps in the information generated, and complementary platforms are essential to obtain more comprehensive data sets relating to specific biological process, such as responses to environmental perturbations in plant systems. The investigation of transcriptional regulation raises different challenges, particularly in associating differentially expressed transcription factors with their downstream responsive genes. In this paper, we discuss the integration of transcriptional factor studies through RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation sequencing (ChIP-seq). We show how the data from ChIP-seq can strengthen information generated from RNA-seq in elucidating gene regulatory mechanisms. In particular, we discuss how integration of ChIP-seq and RNA-seq data can help to unravel transcriptional regulatory networks. This review discusses recent advances in methods for studying transcriptional regulation using these two methods. It also provides guidelines for making choices in selecting specific protocols in RNA-seq pipelines for genome-wide analysis to achieve more detailed characterization of specific transcription regulatory pathways via ChIP-seq.

scholarly journals A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Elisabetta Sauta ◽  
Andrea Demartini ◽  
Francesca Vitali ◽  
Alberto Riva ◽  
Riccardo Bellazzi
Keyword(s):  
Data Fusion ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Bayesian Data Fusion

Structure, evolution and dynamics of transcriptional regulatory networks

2010 ◽  
Vol 38 (5) ◽  
pp. 1155-1178 ◽  
Author(s):  
M. Madan Babu
Keyword(s):  
Regulatory Networks ◽  
Target Genes ◽  
Network Evolution ◽  
Structure Evolution ◽  
Future Research ◽  
Regulation System ◽  
Transcriptional Networks ◽  
Transcriptional Regulatory Networks ◽  
Entire Genome ◽  
Transcriptional Regulatory

The availability of entire genome sequences and the wealth of literature on gene regulation have enabled researchers to model an organism's transcriptional regulation system in the form of a network. In such a network, TFs (transcription factors) and TGs (target genes) are represented as nodes and regulatory interactions between TFs and TGs are represented as directed links. In the present review, I address the following topics pertaining to transcriptional regulatory networks. (i) Structure and organization: first, I introduce the concept of networks and discuss our understanding of the structure and organization of transcriptional networks. (ii) Evolution: I then describe the different mechanisms and forces that influence network evolution and shape network structure. (iii) Dynamics: I discuss studies that have integrated information on dynamics such as mRNA abundance or half-life, with data on transcriptional network in order to elucidate general principles of regulatory network dynamics. In particular, I discuss how cell-to-cell variability in the expression level of TFs could permit differential utilization of the same underlying network by distinct members of a genetically identical cell population. Finally, I conclude by discussing open questions for future research and highlighting the implications for evolution, development, disease and applications such as genetic engineering.

Condensing Biochemistry into Gene Regulatory Networks

2014 ◽  
Vol 4 (3) ◽  
pp. 1-25
Author(s):  
Alberto de la Fuente
Keyword(s):  
Gene Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Models ◽  
Transcriptional Regulatory Networks ◽  
Modern Biology ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Biochemical Systems ◽  
Gene Regulatory

Gene Regulatory Networks are models of gene regulation. Inferring such model from genome-wide gene-expression measurements is one of the key challenges in modern biology, and a large number of algorithms have been proposed for this task. As there is still much confusion in the current literature as to what precisely Gene Regulatory Networks are, it is important to provide a definition that is as unambiguous as possible. In this paper the author provides such a definition and explain what Gene Regulatory Networks are in terms of the underlying biochemical processes. The author will use a linear approximation to the in general non-linear kinetics underlying interactions in biochemical systems and show how a biochemical system can be ‘condensed' into a more compact description, i.e. Gene Regulatory Networks. Important differences between the defined Gene Regulatory Networks and other network models for gene regulation, i.e. Transcriptional Regulatory Networks and Co-Expression Networks, are also discussed.

scholarly journals Consensus transcriptional regulatory networks of coronavirus-infected human cells

2020 ◽  
Author(s):  
Scott A Ochsner ◽  
Rudolf T Pillich ◽  
Neil J McKenna
Keyword(s):  
Signaling Pathways ◽  
Regulatory Networks ◽  
Data Exchange ◽  
Epithelial To Mesenchymal Transition ◽  
Target Genes ◽  
Transcriptional Regulatory Networks ◽  
Mesenchymal Transition ◽  
Human Genes ◽  
Transcriptional Regulatory ◽  
Genes Encoding

AbstractEstablishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of CoV infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family target genes encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.

scholarly journals Genome-wide screening of upstream transcription factors using an expression library

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 51
Author(s):  
Naoya Yahagi ◽  
Yoshinori Takeuchi
Keyword(s):  
Transcription Factors ◽  
Regulatory Networks ◽  
Dependent Manner ◽  
Expression Library ◽  
Transcriptional Regulatory Networks ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Powerful Approach ◽  
Transcription Factor Expression ◽  
Expression Cdna Library

The identification of upstream transcription factors regulating the expression of a gene is generally not an easy process.  To facilitate this task, we constructed an expression cDNA library named Transcription Factor Expression Library (TFEL), which is composed of nearly all the transcription factors in the mouse genome. Genome-wide screening using this library (TFEL scan method) enables us to easily identify transcription factors controlling any given promoter or enhancer of interest in a chromosomal context-dependent manner. Thus, TFEL scan method is a powerful approach to explore transcriptional regulatory networks.

scholarly journals osr1 couples intermediate mesoderm cell fate with temporal dynamics of vessel progenitor cell differentiation

Development ◽  
2021 ◽  
Author(s):  
Elliot A. Perens ◽  
Jessyka T. Diaz ◽  
Agathe Quesnel ◽  
Amjad Askary ◽  
J. Gage Crump ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Temporal Dynamics ◽  
Bhlh Transcription Factor ◽  
Transcriptional Regulatory Networks ◽  
Mesoderm Cell ◽  
Intermediate Mesoderm ◽  
Transcriptional Regulatory ◽  
Mutant Phenotypes

Transcriptional regulatory networks refine gene expression boundaries to define the dimensions of organ progenitor territories. Kidney progenitors originate within the intermediate mesoderm (IM), but the pathways that establish the boundary between the IM and neighboring vessel progenitors are poorly understood. Here, we delineate roles for the zinc finger transcription factor Osr1 in kidney and vessel progenitor development. Zebrafish osr1 mutants display decreased IM formation and premature emergence of lateral vessel progenitors (LVPs). These phenotypes contrast with the increased IM and absent LVPs observed with loss of the bHLH transcription factor Hand2, and loss of hand2 partially suppresses osr1 mutant phenotypes. hand2 and osr1 are expressed together in the posterior mesoderm, but osr1 expression decreases dramatically prior to LVP emergence. Overexpressing osr1 during this timeframe inhibits LVP development while enhancing IM formation and can rescue the osr1 mutant phenotype. Together, our data demonstrate that osr1 modulates the extent of IM formation and the temporal dynamics of LVP development, suggesting that a balance between levels of osr1 and hand2 expression is essential to demarcate the kidney and vessel progenitor territories.

Sign in / Sign up

Export Citation Format

Share Document