Evidence map›Paper›PMID 32101750›Full record

ArticleCell reports2020

Dissecting the Regulatory Strategies of NF-κB RelA Target Genes in the Inflammatory Response Reveals Differential Transactivation Logics.

Kim A Ngo, Kensei Kishimoto, Jeremy Davis-Turak, Aditya Pimplaskar, Zhang Cheng, Roberto Spreafico, Emily Y Chen, Amy Tam, Gourisankar Ghosh, Simon Mitchell and 1 more

Open access · goldAbstract read
In one paragraph

Article in Cell reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
2.9field-weighted citation impact, top 7% 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

50 citing papers in PubMed, 70 citations in OpenAlex.

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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

11 authors at 2 institutions in 1 country.

Kim A NgoSignaling Systems Laboratory, Department of Microbiology Immunology, and Molecular Genetics (MIMG), Institute for Quantitative and Computational Biosciences (QCB), Molecular Biology Institute (MBI), University of California, Los Angeles, Los Angeles, CA 90095, USA.
Kensei KishimotoSignaling Systems Laboratory, Department of Microbiology Immunology, and Molecular Genetics (MIMG), Institute for Quantitative and Computational Biosciences (QCB), Molecular Biology Institute (MBI), University of California, Los Angeles, Los Angeles, CA 90095, USA.
Jeremy Davis-TurakSignaling Systems Laboratory, Department of Microbiology Immunology, and Molecular Genetics (MIMG), Institute for Quantitative and Computational Biosciences (QCB), Molecular Biology Institute (MBI), University of California, Los Angeles, Los Angeles, CA 90095, USA.
Aditya PimplaskarSignaling Systems Laboratory, Department of Microbiology Immunology, and Molecular Genetics (MIMG), Institute for Quantitative and Computational Biosciences (QCB), Molecular Biology Institute (MBI), University of California, Los Angeles, Los Angeles, CA 90095, USA.
Zhang ChengSignaling Systems Laboratory, Department of Microbiology Immunology, and Molecular Genetics (MIMG), Institute for Quantitative and Computational Biosciences (QCB), Molecular Biology Institute (MBI), University of California, Los Angeles, Los Angeles, CA 90095, USA.
Roberto SpreaficoSignaling Systems Laboratory, Department of Microbiology Immunology, and Molecular Genetics (MIMG), Institute for Quantitative and Computational Biosciences (QCB), Molecular Biology Institute (MBI), University of California, Los Angeles, Los Angeles, CA 90095, USA.
Emily Y ChenSignaling Systems Laboratory, Department of Microbiology Immunology, and Molecular Genetics (MIMG), Institute for Quantitative and Computational Biosciences (QCB), Molecular Biology Institute (MBI), University of California, Los Angeles, Los Angeles, CA 90095, USA.
Amy TamSignaling Systems Laboratory, Department of Microbiology Immunology, and Molecular Genetics (MIMG), Institute for Quantitative and Computational Biosciences (QCB), Molecular Biology Institute (MBI), University of California, Los Angeles, Los Angeles, CA 90095, USA.
Gourisankar GhoshDepartment of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92037, USA.
Simon MitchellSignaling Systems Laboratory, Department of Microbiology Immunology, and Molecular Genetics (MIMG), Institute for Quantitative and Computational Biosciences (QCB), Molecular Biology Institute (MBI), University of California, Los Angeles, Los Angeles, CA 90095, USA.
Alexander HoffmannSignaling Systems Laboratory, Department of Microbiology Immunology, and Molecular Genetics (MIMG), Institute for Quantitative and Computational Biosciences (QCB), Molecular Biology Institute (MBI), University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: ahoffmann@ucla.edu.
QB3 · USUniversity of California San Diego · US

Funding

Understanding dynamical coding by NFkBR01GM117134 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HOFFMANN, ALEXANDER, WOLLMAN, ROY · 2016 to 2019
$2.4M
Regulation of signaling via the lkB-NF-kB interactionR01GM071573 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HOFFMANN, ALEXANDER · 2005 to 2013
$2.2M
NFkB Signaling in MacrophagesR01AI127864 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HOFFMANN, ALEXANDER · 2017 to 2021
$1.9M
Undergraduate Research Experience in Neuropsychiatric GenomicsR25MH109172 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ESKIN, ELEAZAR, FREIMER, NELSON B. · 2016 to 2020
$429k
NIAID NIH HHS R01 AI127864NIGMS NIH HHS R01 GM071573NIGMS NIH HHS R01 GM117134NIMH NIH HHS R25 MH109172
6 · The paper itself

Abstract

Nuclear factor κB (NF-κB) RelA is the potent transcriptional activator of inflammatory response genes. We stringently defined a list of direct RelA target genes by integrating physical (chromatin immunoprecipitation sequencing [ChIP-seq]) and functional (RNA sequencing [RNA-seq] in knockouts) datasets. We then dissected each gene's regulatory strategy by testing RelA variants in a primary-cell genetic-complementation assay. All endogenous target genes require RelA to make DNA-base-specific contacts, and none are activatable by the DNA binding domain alone. However, endogenous target genes differ widely in how they employ the two transactivation domains. Through model-aided analysis of the dynamic time-course data, we reveal the gene-specific synergy and redundancy of TA1 and TA2. Given that post-translational modifications control TA1 activity and intrinsic affinity for coactivators determines TA2 activity, the differential TA logics suggests context-dependent versus context-independent control of endogenous RelA-target genes. Although some inflammatory initiators appear to require co-stimulatory TA1 activation, inflammatory resolvers are a part of the NF-κB RelA core response.

Indexed as

AnimalsBase SequenceEmbryo, MammalianFibroblastsInflammationLogicMice, Inbred C57BLModels, BiologicalProtein DomainsTranscriptional ActivationTranscription Factor RelATumor Necrosis Factor-alphaTranscription Factor RelATumor Necrosis Factor-alphagene regulatory strategiesimmune responseinflammatory responsemathematical modelingNF-kappaBNF-κBstructure-function analysissystems biologytranscriptional activationtranscriptional synergy

Identifiers

PMID32101750
PMCPMC7061728
OpenAlexW3008269837

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.