High-throughput analyses and Bayesian network modeling highlight novel epigenetic Adverse Outcome Pathway networks of DNA methyltransferase inhibitor mediated transgenerational effectsShow others and affiliations
2021 (English)In: Journal of Hazardous Materials, ISSN 0304-3894, E-ISSN 1873-3336, Vol. 408, article id 124490Article in journal (Refereed) Published
Abstract [en]
A number of epigenetic modulating chemicals are known to affect multiple generations of a population from a single ancestral exposure, thus posing transgenerational hazards. The present study aimed to establish a high-throughput (HT) analytical workflow for cost-efficient concentration-response analysis of epigenetic and phenotypic effects, and to support the development of novel Adverse Outcome Pathway (AOP) networks for DNA methyltransferase (DNMT) inhibitor-mediated transgenerational effects on aquatic organisms. The model DNMT inhibitor 5-azacytidine (5AC) and the model freshwater crustacean Daphnia magna were used to generate new experimental data and served as prototypes to construct AOPs for aquatic organisms. Targeted HT bioassays (DNMT ELISA, MS-HRM and qPCR) in combination with multigenerational ecotoxicity tests revealed concentration-dependent transgenerational (F0-F3) effects of 5AC on total DNMT activity, DNA promoter methylation, gene body methylation, gene transcription and reproduction. Top sensitive toxicity pathways related to 5AC exposure, such as apoptosis and DNA damage responses were identified in both F0 and F3 using Gaussian Bayesian network modeling. Two novel epigenetic AOP networks on DNMT inhibitor mediated one-generational and transgenerational effects were developed for aquatic organisms and assessed for the weight of evidence. The new HT analytical workflow and AOPs can facilitate future ecological hazard assessment of epigenetic modulating chemicals.
Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 408, article id 124490
Keywords [en]
5-Azacytidine, AOP, DNA methylation, Daphnia, Quantitative response-response relationships, Weight of evidence
National Category
Bioinformatics and Computational Biology
Identifiers
URN: urn:nbn:se:oru:diva-87454DOI: 10.1016/j.jhazmat.2020.124490ISI: 000620381800008PubMedID: 33199140Scopus ID: 2-s2.0-85096198771OAI: oai:DiVA.org:oru-87454DiVA, id: diva2:1501893
Note
Funding Agencies:
NIVA institutional funding scheme "Strategic Institutional Initiatives programme (SIS) for Restoration Ecology"
Research Council of Norway 301397
NIVA Computational Toxicology Program (NCTP)
FWO Science Foundation Flanders
2020-11-182020-11-182025-02-07Bibliographically approved