Using potential master regulator sites and paralogous expansion to construct tissue-specific transcriptional networks

Haubrock, Martin; Li, Jie; Wingender, Edgar

doi:10.1186/1752-0509-6-s2-s15

Cited by 5 publications

(6 citation statements)

References 20 publications

(20 reference statements)

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“…It may therefore be tempting to speculate in the light of the findings reported here, that at least a certain subset of such genes is tagged by NF-Y bound to a CCAAT dimer and induced by c-Fos and AP-1 recruited p300. The CCAAT dimer with a distance of 31bp would thus match the characteristics of a typical seed site, as we have defined earlier [ 36 ].…”

Section: Discussionmentioning

confidence: 96%

NF-Y Binding Site Architecture Defines a C-Fos Targeted Promoter Class

2016

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ChIP-seq experiments detect the chromatin occupancy of known transcription factors in a genome-wide fashion. The comparisons of several species-specific ChIP-seq libraries done for different transcription factors have revealed a complex combinatorial and context-specific co-localization behavior for the identified binding regions. In this study we have investigated human derived ChIP-seq data to identify common cis-regulatory principles for the human transcription factor c-Fos. We found that in four different cell lines, c-Fos targeted proximal and distal genomic intervals show prevalences for either AP-1 motifs or CCAAT boxes as known binding motifs for the transcription factor NF-Y, and thereby act in a mutually exclusive manner. For proximal regions of co-localized c-Fos and NF-YB binding, we gathered evidence that a characteristic configuration of repeating CCAAT motifs may be responsible for attracting c-Fos, probably provided by a nearby AP-1 bound enhancer. Our results suggest a novel regulatory function of NF-Y in gene-proximal regions. Specific CCAAT dimer repeats bound by the transcription factor NF-Y define this novel cis-regulatory module. Based on this behavior we propose a new enhancer promoter interaction model based on AP-1 motif defined enhancers which interact with CCAAT-box characterized promoter regions.

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Section: Discussionmentioning

confidence: 96%

NF-Y Binding Site Architecture Defines a C-Fos Targeted Promoter Class

2016

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“…We combined promoter scanning with PWMs with a four-genome evolutionary conservation analysis to allocate presumed high-affinity, functionally significant TF binding sites (TFBS) in the 1-kb-upstream regions of all known human genes, referring to the TSSs annotated in RefSeq, and to infer TF-target gene relations (see ( 12 ) for details). Using all available vertebrate matrices of the TRANSFAC matrix library (release 2012.2), we predicted potential TFBSs by applying the Match program ( 33 ) with default minFN (‘minimize false negatives’) thresholds in order to retrieve the maximum of potential TF binding sites that have at least the quality of the TFBSs in the underlying training set of the corresponding matrix.…”

Section: Dna Targeting By Transcription Factorsmentioning

confidence: 99%

“…With the recent updates to be reported here, we have introduced a number of smaller revisions in the structure, added mouse and rat orthologs of the human TFs in the classification, and present an independent ontology of mouse TFs, so far confined to the orthologs of the human TFs. Moreover, the information about TFs targets was enhanced by linking PWMs from a systematic in vitro screen ( 10 ), and lists of target sites and genes predicted with the TRANSFAC ® matrix library ( 11 , 12 ).…”

Section: Introductionmentioning

confidence: 99%

TFClass: a classification of human transcription factors and their rodent orthologs

Wingender¹,

Schoeps²,

Haubrock³

et al. 2014

Nucleic Acids Research

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107

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TFClass aims at classifying eukaryotic transcription factors (TFs) according to their DNA-binding domains (DBDs). For this, a classification schema comprising four generic levels (superclass, class, family and subfamily) was defined that could accommodate all known DNA-binding human TFs. They were assigned to their (sub-)families as instances at two different levels, the corresponding TF genes and individual gene products (protein isoforms). In the present version, all mouse and rat orthologs have been linked to the human TFs, and the mouse orthologs have been arranged in an independent ontology. Many TFs were assigned with typical DNA-binding patterns and positional weight matrices derived from high-throughput in-vitro binding studies. Predicted TF binding sites from human gene upstream sequences are now also attached to each human TF whenever a PWM was available for this factor or one of his paralogs. TFClass is freely available at http://tfclass.bioinf.med.uni-goettingen.de/ through a web interface and for download in OBO format.

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“…Each TF-gene, identified by its official HGNC-defined gene name, was represented as a node, with a directed edge connecting it with its target gene node. Further information about the construction of the regulatory network can be found in our previous manuscript [23].…”

Section: Background Regulatory Networkmentioning

confidence: 99%

Constructing temporal regulatory cascades in the context of development and cell differentiation

Daou¹,

Beißbarth²,

Wingender³

et al. 2020

PLoS ONE

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Cell differentiation is a complex process orchestrated by sets of regulators precisely appearing at certain time points, resulting in regulatory cascades that affect the expression of broader sets of genes, ending up in the formation of different tissues and organ parts. The identification of stage-specific master regulators and the mechanism by which they activate each other is a key to understanding and controlling differentiation, particularly in the fields of tissue regeneration and organoid engineering. Here we present a workflow that combines a comprehensive general regulatory network based on binding site predictions with user-provided temporal gene expression data, to generate a a temporally connected series of stage-specific regulatory networks, which we call a temporal regulatory cascade (TRC). A TRC identifies those regulators that are unique for each time point, resulting in a cascade that shows the emergence of these regulators and regulatory interactions across time. The model was implemented in the form of a user-friendly, visual webtool, that requires no expert knowledge in programming or statistics, making it directly usable for life scientists. In addition to generating TRCs the tool links multiple interactive visual workflows, in which a user can track and investigate further different regulators, target genes, and interactions, directing the tool along the way into biologically sensible results based on the given dataset. We applied the TRC model on two different expression datasets, one based on experiments conducted on human induced pluripotent stem cells (hiPSCs) undergoing differentiation into mature cardiomyocytes and the other based on the differentiation of H1-derived human neuronal precursor cells. The model was successful in identifying previously known and new potential key regulators, in addition to the particular time points with which these regulators are associated, in cardiac and neural development.

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Using potential master regulator sites and paralogous expansion to construct tissue-specific transcriptional networks

Cited by 5 publications

References 20 publications

NF-Y Binding Site Architecture Defines a C-Fos Targeted Promoter Class

NF-Y Binding Site Architecture Defines a C-Fos Targeted Promoter Class

TFClass: a classification of human transcription factors and their rodent orthologs

Constructing temporal regulatory cascades in the context of development and cell differentiation

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