Evidence map›Paper›PMID 41332771›Full record

ArticlebioRxiv : the preprint server for biology2025

Widespread low-affinity motifs enhance chromatin accessibility and regulatory potential in mESCs.

Melanie Weilert, Kaelan J Brennan, Khyati Dalal, Sabrina Krueger, Haining Jiang, Rosa Martinez-Corral, Julia Zeitlinger

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Melanie WeilertStowers Institute for Medical Research, Kansas City, MO, USA.ORCID 0000-0001-8683-4580
Kaelan J BrennanStowers Institute for Medical Research, Kansas City, MO, USA.ORCID 0000-0002-4237-4874
Khyati DalalStowers Institute for Medical Research, Kansas City, MO, USA.ORCID 0000-0002-1867-5079
Sabrina KruegerStowers Institute for Medical Research, Kansas City, MO, USA.ORCID 000-0001-6581-1590
Haining JiangStowers Institute for Medical Research, Kansas City, MO, USA.ORCID 0000-0002-0989-5301
Rosa Martinez-CorralCRG (Barcelona Collaboratorium for Modelling and Predictive Biology), Barcelona, Spain.ORCID 0000-0003-3600-3601
Julia ZeitlingerStowers Institute for Medical Research, Kansas City, MO, USA.ORCID 0000-0002-5172-3335

Funding

A transposase system for integrative ChIP-exo and ATAC-seq analysis at single-cell resolutionR01HG010211 · NHGRI · STOWERS INSTITUTE FOR MEDICAL RESEARCH · PI ZEITLINGER, JULIA · 2018 to 2021
$2.3M
Investigating how transcription factors cooperate and overcome the enhancer nucleosome barrier during embryonic patterningF31HD108901 · NICHD · STOWERS INSTITUTE FOR MEDICAL RESEARCH · PI BRENNAN, KAELAN JOSEPH · 2022 to 2024
$83k
NHGRI NIH HHS R01 HG010211NICHD NIH HHS F31 HD108901
6 · The paper itself

Abstract

Low-affinity transcription factor (TF) motifs are an important element of the cis-regulatory code, yet they are notoriously difficult to map and mechanistically incompletely understood, limiting our ability to interpret non-coding variation in development, evolution, and disease. Here we investigate their role in pioneering and leverage sequence-to-profile models of chromatin accessibility in mouse embryonic stem cells to reliably map and interpret low-affinity motifs across the genome. We find that low-affinity motifs have outsized effects by cooperating with nearby motifs through intra-nucleosomal soft syntax. By modeling nucleosome-mediated cooperativity with a kinetic model, we discover and validate that pioneer cooperativity makes a motif operate at higher pioneering ranges across changing TF concentrations, thereby raising the regulatory potential. These results show that low-affinity motifs can be accurately mapped, shape the properties of developmental enhancers and likely play a widespread role in fine-tuning enhancers during evolution.

Identifiers

PMID41332771
PMCPMC12667796

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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.