In one paragraphArticle in Research square, 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 itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
31 authors.
Shuhua FuWashington University School of Medicine.
Wanqing ShaoWashington University School of Medicine.
Prashant KuntalaWashington University School of Medicine in St. Louis.
Bongsoo ParkJohns Hopkins University.
Rahul JangidBaylor College of Medicine.
Laurie SvobodaUniversity of Michigan.
Shaopeng LiuWashington University School of Med.
Robert HamanakaThe University of Chicago.
Maureen SartorUniversity of Michigan School of Public Health.
Christopher KrappUniversity of Pennsylvania.
Heather PatisaulNorth Carolina State University.
Tim WiltshireUniversity of North Carolina at Chapel Hill.
Shyam BiswalJohns Hopkins University.
Gokhan MutluUniversity of Chicago.
Sanjay RajagopalanCase Western Reserve University.
Wan-Yee TangUniversity of Pittsburgh School of Public Health.
Dana DolinoyUniversity of Michigan School of Public Health.
Funding
The WashU-UCSC-EBI Human Genome Reference Center."U41HG010972 · NHGRI · WASHINGTON UNIVERSITY · PI Ira M Hall, Heng Li · 2019 to 2026
$24.9MStrategic Vision & Impact on Environmental HealthP30ES017885 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Dana Dolinoy · 2011 to 2026
$21.3MTranslational Research Support CoreP30ES030285 · NIEHS · BAYLOR COLLEGE OF MEDICINE · PI Cheryl L. Walker · 2019 to 2026
$14.7MPilot Program CoreP30ES027792 · NIEHS · UNIVERSITY OF CHICAGO · PI Gokhan M. Mutlu, Gail S Prins · 2017 to 2026
$13.6MWashU-Northwestern Genomic Variation and Function Data and Administrative Coordinating CenterU24HG012070 · NHGRI · WASHINGTON UNIVERSITY · PI Ting Wang, Feng Yue · 2021 to 2026
$9.7MEnvironmental Epigenomics and Precision Environmental HealthR35ES031686 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Dana Dolinoy · 2020 to 2026
$6.1MTHE WASHU TARGET ENVIRONMENTAL EPIGENOMICS DATA COORDINATION CENTERU24ES026699 · NIEHS · WASHINGTON UNIVERSITY · PI WANG, TING · 2016 to 2021
$5.2MWashU Somatic Mosaicism across Human Tissues (SMaHT) Program Organizational CenterU24NS132103 · NINDS · WASHINGTON UNIVERSITY · PI FULTON, LUCINDA, LAWSON, HEATHER A. · 2023 to 2025
$4.5MPerinatal Exposures, Tissue- and Cell-specific Epigenomics, & Lifecourse OutcomesU01ES026697 · NIEHS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DOLINOY, DANA · 2016 to 2019
$3.6MEpigenetic Signatures of Developmental Reprogramming in Target and Surrogate TissuesU01ES026719 · NIEHS · TEXAS A&M UNIVERSITY HEALTH SCIENCE CTR · PI BARTOLOMEI, MARISA S., WALKER, CHERYL L. · 2016 to 2020
$3.4MSystems Toxicogenomics of Endocrine Disrupting Chemicals in BrainU01ES026717 · NIEHS · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI AYLOR, DAVID LAWRENCE · 2016 to 2019
$2.9MParticulate matter-induced changes in DNA methylome and transcriptomeU01ES026718 · NIEHS · UNIVERSITY OF CHICAGO · PI MUTLU, GOKHAN M. · 2016 to 2019
$2.5MNHGRI NIH HHS U24 HG012070NHGRI NIH HHS U41 HG010972NIEHS NIH HHS P30 ES017885NIEHS NIH HHS P30 ES027792NIEHS NIH HHS P30 ES030285NIEHS NIH HHS R35 ES031686NIEHS NIH HHS RC2 ES018789NIEHS NIH HHS U01 ES026697NIEHS NIH HHS U01 ES026717NIEHS NIH HHS U01 ES026718NIEHS NIH HHS U01 ES026719NIEHS NIH HHS U24 ES026699NIGMS NIH HHS R35 GM142917NINDS NIH HHS U24 NS132103
6 · The paper itselfAbstract
Exposure to toxic substances, particularly early in life, can perturb epigenomic marks linked to disease susceptibility. Human studies of environmental exposures often rely on surrogate tissues such as blood, but toxicant accumulation differs across organs and results in tissue-specific responses. Thus, understanding whether exposure-induced epigenomic alterations in surrogate tissues such as blood reflect changes in toxicant target tissues, such as liver, is essential for designing and interpreting environmental epigenetic studies. To address this knowledge gap, we systematically analyzed 1,013 multi-omics data from the TaRGET II Consortium, comparing molecular responses in mouse liver and blood following perinatal exposure to arsenic, lead, bisphenol A, tributyltin, di-2-ethylhexyl phthalate, tetrachlorodibenzo-p-dioxin, or air pollution in the form of particulate matter < 2.5μm (PM2.5). Most toxicant-induced molecular changes were tissue-specific, yet we identified a subset of co-regulated genes and regulatory elements in liver and blood in response to early-life exposure to toxicants. Moreover, we discovered that specific pathways, such as immune-related processes, were commonly affected by exposures in both tissues, and transcription factors, including
Indexed as
early-life exposureEnvironmental epigeneticsepigenetic biomarkerssurrogate and target tissueTaRGET II Consortiumtranscriptome
Identifiers
PMID41282178
PMCPMC12632707
What Socratic holds
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