Evidence map›Paper›PMID 37543039›Full record

ArticleCell systems2023

High-throughput functional characterization of combinations of transcriptional activators and repressors.

Adi X Mukund, Josh Tycko, Sage J Allen, Stephanie A Robinson, Cecelia Andrews, Joydeb Sinha, Connor H Ludwig, Kaitlyn Spees, Michael C Bassik, Lacramioara Bintu

Open access · bronzeAbstract read
In one paragraph

Article in Cell systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
5.2field-weighted citation impact, top 4% 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

26 citing papers in PubMed, 34 citations in OpenAlex.

  1. Review
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  10. Emergence of activation or repression in transcriptional control under a fixed molecular context.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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

10 authors at 1 institution in 1 country.

Adi X MukundBiophysics Program, Stanford University, Stanford, CA 94305, USA.
Josh TyckoDepartment of Genetics, Stanford University, Stanford, CA 94305, USA.
Sage J AllenDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Stephanie A RobinsonDepartment of Chemistry, Stanford University, Stanford, CA 94305, USA.
Cecelia AndrewsDepartment of Developmental Biology, Stanford University, Stanford, CA 94305, USA.
Joydeb SinhaDepartment of Chemical and Systems Biology, Stanford University, Stanford, CA 94305, USA.
Connor H LudwigDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Kaitlyn SpeesDepartment of Genetics, Stanford University, Stanford, CA 94305, USA.
Michael C BassikDepartment of Genetics, Stanford University, Stanford, CA 94305, USA.
Lacramioara BintuDepartment of Bioengineering, Stanford University, Stanford, CA 94305, USA. Electronic address: lbintu@stanford.edu.
Stanford University · US

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007365 · NIGMS · STANFORD UNIVERSITY · PI CHUA, KATRIN F · 1985 to 2021
$33.8M
Medical Scientist Training ProgramT32GM145402 · NIGMS · STANFORD UNIVERSITY · PI Katrin F. Chua · 2022 to 2026
$10.0M
High-throughput development and characterization of compact tools for transcriptional and chromatin perturbationsR01HG011866 · NHGRI · STANFORD UNIVERSITY · PI MICHAEL C BASSIK, Lacramioara Bintu · 2021 to 2026
$6.9M
Single-cell analysis and synthetic control of mammalian chromatin dynamics and gene regulationR35GM128947 · NIGMS · STANFORD UNIVERSITY · PI Lacramioara Bintu · 2018 to 2026
$3.1M
Molecular Pharmacology Training GrantT32GM113854 · NIGMS · STANFORD UNIVERSITY · PI MOCHLY-ROSEN, DARIA · 2015 to 2019
$1.1M
High-throughput dissection of transcriptional regulation in kidney diseaseK00DK126120 · NIDDK · HARVARD MEDICAL SCHOOL · PI TYCKO, JOSH · 2022 to 2025
$355k
High-throughput dissection of transcriptional regulation in kidney diseaseF99DK126120 · NIDDK · STANFORD UNIVERSITY · PI TYCKO, JOSH · 2020 to 2021
$78k
NHGRI NIH HHS R01 HG011866NIDDK NIH HHS F99 DK126120NIDDK NIH HHS K00 DK126120NIGMS NIH HHS R35 GM128947NIGMS NIH HHS T32 GM007365NIGMS NIH HHS T32 GM113854NIGMS NIH HHS T32 GM145402
6 · The paper itself

Abstract

Despite growing knowledge of the functions of individual human transcriptional effector domains, much less is understood about how multiple effector domains within the same protein combine to regulate gene expression. Here, we measure transcriptional activity for 8,400 effector domain combinations by recruiting them to reporter genes in human cells. In our assay, weak and moderate activation domains synergize to drive strong gene expression, whereas combining strong activators often results in weaker activation. In contrast, repressors combine linearly and produce full gene silencing, and repressor domains often overpower activation domains. We use this information to build a synthetic transcription factor whose function can be tuned between repression and activation independent of recruitment to target genes by using a small-molecule drug. Altogether, we outline the basic principles of how effector domains combine to regulate gene expression and demonstrate their value in building precise and flexible synthetic biology tools. A record of this paper's transparent peer review process is included in the supplemental information.

Indexed as

Repressor ProteinsTranscription, GeneticGene Expression RegulationGenes, ReporterHumansTranscription FactorsRepressor ProteinsTranscription Factorseukaryotic gene regulationhigh-throughput screeningmammalian synthetic biologysystems biologytranscriptional effector domains

Identifiers

PMID37543039
PMCPMC10642976
OpenAlexW4385576708

What OpenQuestion holds

Textmetadata
LicenceTDM
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.