Evidence map›Paper›PMID 32986742›Full record

ArticlePloS one2020

Mining a human transcriptome database for chemical modulators of NRF2.

John P Rooney, Brian Chorley, Steven Hiemstra, Steven Wink, Xuting Wang, Douglas A Bell, Bob van de Water, J Christopher Corton

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

22 citing papers in PubMed, 52 citations in OpenAlex.

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  19. Evaluating adaptive stress response gene signatures using transcriptomics.Computational toxicology (Amsterdam, Netherlands) · 2021
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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

8 authors at 3 institutions in 2 countries.

John P RooneyCenter for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC, United States of America.
Brian ChorleyCenter for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC, United States of America.
Steven HiemstraDivision of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.
Steven WinkDivision of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.
Xuting WangImmunity, Inflammation, and Disease Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States of America.
Douglas A BellImmunity, Inflammation, and Disease Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States of America.
Bob van de WaterDivision of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands.
J Christopher CortonCenter for Computational Toxicology and Exposure, US-EPA, Research Triangle Park, NC, United States of America.ORCID 0000-0002-6197-2036
Environmental Protection Agency · USLeiden University · NLNational Institute of Environmental Health Sciences · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nuclear factor erythroid-2 related factor 2 (NRF2) encoded by the NFE2L2 gene is a transcription factor critical for protecting cells from chemically-induced oxidative stress. We developed computational procedures to identify chemical modulators of NRF2 in a large database of human microarray data. A gene expression biomarker was built from statistically-filtered gene lists derived from microarray experiments in primary human hepatocytes and cancer cell lines exposed to NRF2-activating chemicals (oltipraz, sulforaphane, CDDO-Im) or in which the NRF2 suppressor Keap1 was knocked down by siRNA. Directionally consistent biomarker genes were further filtered for those dependent on NRF2 using a microarray dataset from cells after NFE2L2 siRNA knockdown. The resulting 143-gene biomarker was evaluated as a predictive tool using the correlation-based Running Fisher algorithm. Using 59 gene expression comparisons from chemically-treated cells with known NRF2 activating potential, the biomarker gave a balanced accuracy of 93%. The biomarker was comprised of many well-known NRF2 target genes (AKR1B10, AKR1C1, NQO1, TXNRD1, SRXN1, GCLC, GCLM), 69% of which were found to be bound directly by NRF2 using ChIP-Seq. NRF2 activity was assessed across ~9840 microarray comparisons from ~1460 studies examining the effects of ~2260 chemicals in human cell lines. A total of 260 and 43 chemicals were found to activate or suppress NRF2, respectively, most of which have not been previously reported to modulate NRF2 activity. Using a NRF2-responsive reporter gene in HepG2 cells, we confirmed the activity of a set of chemicals predicted using the biomarker. The biomarker will be useful for future gene expression screening studies of environmentally-relevant chemicals.

Indexed as

Databases, GeneticData MiningTranscriptomeBiomarkersHep G2 CellsHumansNF-E2-Related Factor 2BiomarkersNFE2L2 protein, humanNF-E2-Related Factor 2

Identifiers

PMID32986742
PMCPMC7521735
OpenAlexW3090701623

What OpenQuestion holds

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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.