Evidence map›Paper›PMID 37925545›Full record

ArticleScientific reports2023

eRNA co-expression network uncovers TF dependency and convergent cooperativity.

Seungha Alisa Lee, Katla Kristjánsdóttir, Hojoong Kwak

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.5field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Seungha Alisa LeeDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14850, USA.
Katla KristjánsdóttirDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14850, USA.
Hojoong KwakDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, NY, 14850, USA. hk572@cornell.edu.
Cornell University · US

Funding

High Resolution Functional Maps of Enhancer RNA and Subcellular RNA Granules in Human Immune CellsR35GM142979 · NIGMS · NEW YORK MEDICAL COLLEGE · PI KWAK, HOJOONG · 2021 to 2025
$2.0M
NIGMS NIH HHS R35 GM142979
6 · The paper itself

Abstract

Enhancer RNAs (eRNAs) are non-coding RNAs produced by transcriptional enhancers that are highly correlated with their activity. Using a capped nascent RNA sequencing (PRO-cap) dataset in human lymphoblastoid cell lines across 67 individuals, we identified inter-individual variation in the expression of over 80 thousand transcribed transcriptional regulatory elements (tTREs), in both enhancers and promoters. Co-expression analysis of eRNAs from tTREs across individuals revealed how enhancers are associated with each other and with promoters. Mid- to long-range co-expression showed a distance-dependent decay that was modified by TF occupancy. In particular, we found a class of "bivalent" TFs, including Cohesin, that both facilitate and isolate the interaction between enhancers and/or promoters, depending on their topology. At short distances, we observed strand-specific correlations between nearby eRNAs in both convergent and divergent orientations. Our results support a cooperative model of convergent eRNAs, consistent with eRNAs facilitating adjacent enhancers rather than interfering with each other. Therefore, our approach to infer functional interactions from co-expression analyses provided novel insights into the principles of enhancer interactions as a function of distance, orientation, and binding landscapes of TFs.

Indexed as

Enhancer Elements, GeneticTranscription, GeneticHumansPromoter Regions, GeneticRegulatory Elements, TranscriptionalRNARNA

Identifiers

PMID37925545
PMCPMC10625640
OpenAlexW4388340346

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.