Evidence map›Paper›PMID 26617673›Full record

ArticleBioData mining2015

Genome-wide predictors of NF-κB recruitment and transcriptional activity.

Marcin Cieślik, Stefan Bekiranov

Abstract read
In one paragraph

Article in BioData mining, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Molecules (Basel, Switzerland) · 2022
    Article
  3. Review
  4. Review
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  8. Article
  9. Conditional Activation of NF-κB Inducing Kinase (NIK) in the Osteolineage Enhances Both Basal and Loading-Induced Bone Formation.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2019
    Article
  10. Review
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Marcin CieślikDepartment of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia USA ; Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, Michigan 48109 USA.
Stefan BekiranovDepartment of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, Virginia USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInducible transcription factors (TFs) mediate transcriptional responses to environmental cues. In response to multiple inflammatory signals active NF-κB dimers enter the nucleus and trigger cell-type-, and stimulus-specific transcriptional programs. Although much is known about NF-κB inducing pathways and about locus-specific mechanisms of transcriptional control, it is poorly understood how the pre-existing chromatin landscape determines NF-κB target selection and activation. Specifically, it is not known which epigenetic marks and pre-bound TFs serve genome-wide as positive (negative) cues for active NF-κB.

resultsWe applied multivariate and combinatorial data mining techniques on a comprehensive dataset of DNA methylation, DNase I hypersensitivity, eight epigenetic marks, and 34 TFs to arrive at genome-wide patterns that predict NF-κB binding. Strikingly, we observed NF-κB recruitment to accessible and nucleosome-bound sites. Within nucleosomal DNA NF-κB binding was primed by H3K4me1 and H2A.Z, but also hyper-methylated DNA outside of promoters and CpG-islands. Many of these predictors showed combinatorial cooperativity and statistically significant interactions. Recruitment to pre-accessible sites was more frequent and influenced by chromatin-associated TFs. We observed that specific TF-combinations are greatly enriched for (or depleted of) NF-κB binding events.

conclusionsWe provide evidence of NF-κB binding within genomic regions that lack classical marks of activity. These pioneer binding events are relatively often associated with transcriptional regulation. Further, our predictive models indicate that specific combinations of epigenetic marks and transcription factors predetermine the NF-κB cistrome, supporting the feasibility of using statistical approaches to identify "histone codes".

Identifiers

PMID26617673
PMCPMC4661973

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.