Evidence map›Paper›PMID 40488133›Full record

ArticlebioRxiv : the preprint server for biology2025

Missense variants in human forkhead transcription factors reveal determinants of forkhead DNA bispecificity.

Jessica King, Stephen S Gisselbrecht, Julie-Alexia Dias, Raehoon Jeong, Elisabeth Rothman, Martha L Bulyk

Abstract readPreprint
PubMed Full text at PMC Publisher
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

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

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

6 authors.

Jessica KingDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
Stephen S GisselbrechtDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
Julie-Alexia DiasComputational Biology and Quantitative Genetics Program, Harvard T.H. Chan School of Public Health Boston, MA, USA.
Raehoon JeongDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
Elisabeth RothmanDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
Martha L BulykDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, USA.ORCID 0000-0002-3456-4555

Funding

Impact of Coding Variation on Transcription Factor - DNA Recognition_Diversity SupplementR01HG010501 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI MARTHA L BULYK · 2019 to 2026
$6.9M
NHGRI NIH HHS R01 HG010501
6 · The paper itself

Abstract

Recognition of specific DNA sequences by transcription factors (TFs) is a key step in transcriptional control of gene expression. While most forkhead (FH) TFs bind either an FKH (RYAAAYA) or an FHL (GACGC) recognition motif, some FHs can bind both motifs. Mechanisms that control whether a FH is monospecific versus bispecific have remained unknown. Screening a library of 12 reference FH proteins, 61 naturally occurring missense variants including clinical variants, and 22 designed mutant FHs for DNA binding activity using universal ("all 10-mer") protein binding microarrays (PBMs) revealed non-DNA-contacting residues that control mono- versus bispecificity. Variation in non-DNA-contacting amino acid residues of TFs is associated with human traits and may play a role in the evolution of TF DNA binding activities and gene regulatory networks.

Indexed as

bispecificitycoding variantsDNA binding activityForkheadprotein binding microarraysrare variantsspecificity determinantstranscription factortranscription factor-DNA recognition

Identifiers

PMID40488133
PMCPMC12143875

What OpenQuestion holds

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Registered trials

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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.