Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1021/acs.est.3c10814 |
| Titel (primär) | Deep learning bridged bioactivity, structure, and GC-HRMS-readable evidence to decipher nontarget toxicants in sediments |
| Autor | Cheng, F.; Escher, B.I.
; Li, H.; König, M.; Tong, Y.; Huang, J.; He, L.; Wu, X.; Lou, X.; Wang, D.; Wu, F.; Pei, Y.; Yu, Z.; Brooks, B.W.; Zeng, E.Y.; You, J. |
| Quelle | Environmental Science & Technology |
| Erscheinungsjahr | 2024 |
| Department | ZELLTOX |
| Band/Volume | 58 |
| Heft | 35 |
| Seite von | 15415 |
| Seite bis | 15427 |
| Sprache | englisch |
| Topic | T9 Healthy Planet |
| Supplements | https://pubs.acs.org/doi/suppl/10.1021/acs.est.3c10814/suppl_file/es3c10814_si_001.pdf https://pubs.acs.org/doi/suppl/10.1021/acs.est.3c10814/suppl_file/es3c10814_si_002.xlsx |
| Keywords | Mixture risk assessment; high-throughput screening bioassays; nontarget analysis; artificial intelligence; effect-directed analysis; event-driven taxonomy (EDT); high resolution mass spectrometry (HRMS); event driver ions (EDION) |
| Abstract | Identifying causative toxicants in mixtures is critical, but this task is challenging when mixtures contain multiple chemical classes. Effect-based methods are used to complement chemical analyses to identify toxicants, yet conventional bioassays typically rely on an apical and/or single endpoint, providing limited diagnostic potential to guide chemical prioritization. We proposed an event-driven taxonomy framework for mixture risk assessment that relied on high-throughput screening bioassays and toxicant identification integrated by deep learning. In this work, the framework was evaluated using chemical mixtures in sediments eliciting aryl-hydrocarbon receptor activation and oxidative stress response. Mixture prediction using target analysis explained <10% of observed sediment bioactivity. To identify additional contaminants, two deep learning models were developed to predict fingerprints of a pool of bioactive substances (event driver fingerprint, EDFP) and convert these candidates to MS-readable information (event driver ion, EDION) for nontarget analysis. Two libraries with 121 and 118 fingerprints were established, and 247 bioactive compounds were identified at confidence level 2 or 3 in sediment extract using GC-qToF-MS. Among them, 12 toxicants were analytically confirmed using reference standards. Collectively, we present a “bioactivity-signature-toxicant” strategy to deconvolute mixtures and to connect patchy data sets and guide nontarget analysis for diverse chemicals that elicit the same bioactivity. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29231 |
| Cheng, F., Escher, B.I., Li, H., König, M., Tong, Y., Huang, J., He, L., Wu, X., Lou, X., Wang, D., Wu, F., Pei, Y., Yu, Z., Brooks, B.W., Zeng, E.Y., You, J. (2024): Deep learning bridged bioactivity, structure, and GC-HRMS-readable evidence to decipher nontarget toxicants in sediments Environ. Sci. Technol. 58 (35), 15415 - 15427 10.1021/acs.est.3c10814 |
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