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Comprehensive Evaluation System for Post-Metabolic Activity of Potential Thyroid-Disrupting Chemicals

  • Yurim Jang (Interdisciplinary Program in Agricultural Genomics, Seoul National University) ;
  • Ji Hyun Moon (Department of Agricultural Biotechnology, Seoul National University) ;
  • Byung Kwan Jeon (Department of Bio and Fermentation Convergence Technology, Kookmin University) ;
  • Ho Jin Park (Department of Food Science and Biotechnology, Chung-Ang University) ;
  • Hong Jin Lee (Department of Food Science and Biotechnology, Chung-Ang University) ;
  • Do Yup Lee (Interdisciplinary Program in Agricultural Genomics, Seoul National University)
  • Received : 2023.01.28
  • Accepted : 2023.05.26
  • Published : 2023.10.28

Abstract

Endocrine-disrupting chemicals (EDCs) are compounds that disturb hormonal homeostasis by binding to receptors. EDCs are metabolized through hepatic enzymes, causing altered transcriptional activities of hormone receptors, and thus necessitating the exploration of the potential endocrine-disrupting activities of EDC-derived metabolites. Accordingly, we have developed an integrative workflow for evaluating the post-metabolic activity of potential hazardous compounds. The system facilitates the identification of metabolites that exert hormonal disruption through the integrative application of an MS/MS similarity network and predictive biotransformation based on known hepatic enzymatic reactions. As proof-of-concept, the transcriptional activities of 13 chemicals were evaluated by applying the in vitro metabolic module (S9 fraction). Identified among the tested chemicals were three thyroid hormone receptor (THR) agonistic compounds that showed increased transcriptional activities after phase I+II reactions (T3, 309.1 ± 17.3%; DITPA, 30.7 ± 1.8%; GC-1, 160.6 ± 8.6% to the corresponding parents). The metabolic profiles of these three compounds showed common biotransformation patterns, particularly in the phase II reactions (glucuronide conjugation, sulfation, GSH conjugation, and amino acid conjugation). Data-dependent exploration based on molecular network analysis of T3 profiles revealed that lipids and lipid-like molecules were the most enriched biotransformants. The subsequent subnetwork analysis proposed 14 additional features, including T4 in addition to 9 metabolized compounds that were annotated by prediction system based on possible hepatic enzymatic reaction. The other 10 THR agonistic negative compounds showed unique biotransformation patterns according to structural commonality, which corresponded to previous in vivo studies. Our evaluation system demonstrated highly predictive and accurate performance in determining the potential thyroid-disrupting activity of EDC-derived metabolites and for proposing novel biotransformants.

Keywords

Acknowledgement

This research was supported by a grant (19162MFDS099, 20163MFDS120) from the Ministry of Food and Drug Safety and by the Bio Industrial Technology Development Program (20018494, Discovery of new microbiome-based gut-liver axis control biomaterials and development of commercialization technology to overcome non-alcoholic fatty liver disease) from the Ministry of Trade, Industry and Energy (MOTIE, Korea).

References

  1. Tabb MM, Blumberg B. 2006. New modes of action for endocrine-disrupting chemicals. Mol. Endocrinol. 20: 475-482.  https://doi.org/10.1210/me.2004-0513
  2. Lee HR, Jeung EB, Cho MH, Kim TH, Leung PC, Choi KC. 2013. Molecular mechanism (s) of endocrine-disrupting chemicals and their potent oestrogenicity in diverse cells and tissues that express oestrogen receptors. J. Cell. Mol. Med. 17: 1-11.  https://doi.org/10.1111/j.1582-4934.2012.01649.x
  3. Schug TT, Janesick A, Blumberg B, Heindel JJ. 2011. Endocrine disrupting chemicals and disease susceptibility. J. Steroid Biochem. Mol. Biol. 127: 204-215.  https://doi.org/10.1016/j.jsbmb.2011.08.007
  4. Lauretta R, Sansone A, Sansone M, Romanelli F, Appetecchia M. 2019. Endocrine disrupting chemicals: effects on endocrine glands. Front. Endocrinol. 10: 178. 
  5. OECD. 2018. OECD in vitro screens (Conceptual Framework Level 2), Ed. 
  6. Gelbke H, Kayser M, Poole A. 2004. OECD test strategies and methods for endocrine disruptors. Toxicology 205: 17-25.  https://doi.org/10.1016/j.tox.2004.06.034
  7. Bernal J. 2002. Action of thyroid hormone in brain. J. Endocrinol. Investig. 25: 268-288.  https://doi.org/10.1007/BF03344003
  8. Boas M, Feldt-Rasmussen U, Main KM. 2012. Thyroid effects of endocrine disrupting chemicals. Mol. Cell. Endocrinol. 355: 240-248.  https://doi.org/10.1016/j.mce.2011.09.005
  9. Browne P, Van Der Wal L, Gourmelon A. 2020. OECD approaches and considerations for regulatory evaluation of endocrine disruptors. Mol. Cell. Endocrinol. 504: 110675. 
  10. Jeon BK, Jang Y, Lee EM, Moon JH, Lee HJ, Lee DY. 2021. A systematic approach to metabolic characterization of thyroid-disrupting chemicals and their in vitro biotransformants based on prediction-assisted metabolomic analysis. J. Chromatogr. A 1649: 462222. 
  11. J Richardson S, Bai A, A Kulkarni A, F Moghaddam M. 2016. Efficiency in drug discovery: liver S9 fraction assay as a screen for metabolic stability. Drug Metab. Lett. 10: 83-90.  https://doi.org/10.2174/1872312810666160223121836
  12. Doke SK, Dhawale SC. 2015. Alternatives to animal testing: a review. Saudi Pharm. J. 23: 223-229.  https://doi.org/10.1016/j.jsps.2013.11.002
  13. Lee SH, Seo H, Byrd N, Willett C, Lee HS, Park Y. 2022. Determination of thyroidal endocrine-disrupting chemicals (EDCs) activities using a human cell-based transactivation assay. Environ. Sci. Eur. 34: 51. 
  14. Lee S-H, Seo H, Lee H-S, Park Y. 2020. Development and characterization of a human cell line-based transactivation assay to assess thyroid EDCs. Environ. Res. 182: 109110. 
  15. van Vugt-Lussenburg BM, van der Lee RB, Man H-Y, Middelhof I, Brouwer A, Besselink H, et al. 2018. Incorporation of metabolic enzymes to improve predictivity of reporter gene assay results for estrogenic and anti-androgenic activity. Reprod. Toxicol. 75: 40-48.  https://doi.org/10.1016/j.reprotox.2017.11.005
  16. Park SJ, Kim JK, Kim H-H, Yoon BA, Ji DY, Lee CW, et al. 2019. Integrative metabolomics reveals unique metabolic traits in Guillain-Barre syndrome and its variants. Sci. Rep. 9: 1077. 
  17. Yu JS, Youn GS, Choi J, Kim CH, Kim BY, Yang SJ, et al. 2021. Lactobacillus lactis and Pediococcus pentosaceus-driven reprogramming of gut microbiome and metabolome ameliorates the progression of non-alcoholic fatty liver disease. Clin. Transl. Med. 11: e634. 
  18. Park SJ, Lee J, Lee S, Lim S, Noh J, Cho SY, et al. 2020. Exposure of ultrafine particulate matter causes glutathione redox imbalance in the hippocampus: A neurometabolic susceptibility to Alzheimer's pathology. Sci. Total Environ. 718: 137267. 
  19. Cerrato A, Cannazza G, Capriotti AL, Citti C, La Barbera G, Lagana A, et al. 2020. A new software-assisted analytical workflow based on high-resolution mass spectrometry for the systematic study of phenolic compounds in complex matrices. Talanta 209: 120573. 
  20. Montone CM, Cerrato A, Botta B, Cannazza G, Capriotti AL, Cavaliere C, et al. 2020. Improved identification of phytocannabinoids using a dedicated structure-based workflow. Talanta 219: 121310. 
  21. Simonato M, Fochi I, Vedovelli L, Giambelluca S, Carollo C, Padalino M, et al. 2019. Urinary metabolomics reveals kynurenine pathway perturbation in newborns with transposition of great arteries after surgical repair. Metabolomics 15: 145. 
  22. Wang X, Chang X, Luo X, Su M, Xu R, Chen J, et al. 2019. An integrated approach to characterize intestinal metabolites of four phenylethanoid glycosides and intestinal microbe-mediated antioxidant activity evaluation in vitro using UHPLC-Q-Exactive High-Resolution Mass Spectrometry and a 1, 1-Diphenyl-2-picrylhydrazyl-based assay. Front. Pharmacol. 10: 826. 
  23. Howe E, Holton K, Nair S, Schlauch D, Sinha R, Quackenbush J. 2010. Mev: multiexperiment viewer, pp. 267-277. Biomedical informatics for cancer research, Ed. Springer. 
  24. Nothias LF, Petras D, Schmid R, Duhrkop K, Rainer J, Sarvepalli A, et al. 2020. Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17: 905-908.  https://doi.org/10.1038/s41592-020-0933-6
  25. Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, et al. 2016. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat. Biotechnol. 34: 828-837.  https://doi.org/10.1038/nbt.3597
  26. Aron AT, Gentry EC, McPhail KL, Nothias LF, Nothias-Esposito M, Bouslimani A, et al. 2020. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat. Protocols 15: 1954-1991.  https://doi.org/10.1038/s41596-020-0317-5
  27. Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, et al. 2015. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat. Methods 12: 523-526.  https://doi.org/10.1038/nmeth.3393
  28. Neto FC, Raftery D. 2021. Expanding urinary metabolite annotation through integrated mass spectral similarity networking. Anal. Chem. 93: 12001-12010.  https://doi.org/10.1021/acs.analchem.1c02041
  29. Ernst M, Kang KB, Caraballo-Rodriguez AM, Nothias LF, Wandy J, Chen C, et al. 2019. MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools. Metabolites 9: 144. 
  30. Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, et al. 2016. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J. Cheminform. 8: 61. 
  31. Djoumbou-Feunang Y, Fiamoncini J, Gil-de-la-Fuente A, Greiner R, Manach C, Wishart DS. 2019. BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J. Cheminform. 11: 2. 
  32. Ridder L, Wagener M. 2008. SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem 3: 821-832.  https://doi.org/10.1002/cmdc.200700312
  33. Tong Z, Li H, Goljer I, McConnell O, Chandrasekaran A. 2007. In vitro glucuronidation of thyroxine and triiodothyronine by liver microsomes and recombinant human UDP-glucuronosyltransferases. Drug Metab. Dispos. 35: 2203-2210.  https://doi.org/10.1124/dmd.107.016972
  34. Moreno M, de Lange P, Lombardi A, Silvestri E, Lanni A, Goglia F. 2008. Metabolic effects of thyroid hormone derivatives. Thyroid 18: 239-253.  https://doi.org/10.1089/thy.2007.0248
  35. Knights KM, Sykes MJ, Miners JO. 2007. Amino acid conjugation: contribution to the metabolism and toxicity of xenobiotic carboxylic acids. Exp. Opin. Drug Metab. Txicol. 3: 159-168.  https://doi.org/10.1517/17425255.3.2.159
  36. Mondal S, Raja K, Schweizer U, Mugesh G. 2016. Chemistry and biology in the biosynthesis and action of thyroid hormones. Angew. Chemi. Int. Ed. Engl.55: 7606-7630.  https://doi.org/10.1002/anie.201601116
  37. Flock EV, Bollman JL, Stobie GH. 1962. Metabolic pathways of tetraiodothyroacetic acid, triiodothyroacetic acid, tetraiodothyropropionic acid and triiodothyropropionic acid. Biochem. Pharmacol. 11: 627-637.  https://doi.org/10.1016/0006-2952(62)90124-7
  38. Quinn RA, Nothias LF, Vining O, Meehan M, Esquenazi E, Dorrestein PC. 2017. Molecular networking as a drug discovery, drug metabolism, and precision medicine strategy. Trends Pharmacol. Sci. 38: 143-154.  https://doi.org/10.1016/j.tips.2016.10.011
  39. Yu JS, Nothias LF, Wang M, Kim DH, Dorrestein PC, Kang KB, et al. 2022. Tandem mass spectrometry molecular networking as a powerful and efficient tool for drug metabolism studies. Anal. Chem. 94: 1456-1464.  https://doi.org/10.1021/acs.analchem.1c04925
  40. Taurog A, Dorris ML. 1992. Myeloperoxidase-catalyzed iodination and coupling. Arch. Biochem. Biophys. 296: 239-246.  https://doi.org/10.1016/0003-9861(92)90568-H
  41. La Perle KM, Jhiang SM. 2003. Iodine: Symporter and oxidation, thyroid hormone biosynthesis. Thyroid Hormone Biosynthesis 2023: 517-522.  https://doi.org/10.1016/B0-12-341103-3/00180-7
  42. Kang JH, Katayama Y, Kondo F. 2006. Biodegradation or metabolism of bisphenol A: from microorganisms to mammals. Toxicology 217: 81-90.  https://doi.org/10.1016/j.tox.2005.10.001
  43. Schlecht C, Klammer H, Frauendorf H, Wuttke W, Jarry H. 2008. Pharmacokinetics and metabolism of benzophenone 2 in the rat. Toxicology 245: 11-17.  https://doi.org/10.1016/j.tox.2007.12.015
  44. Mueck AO, Seeger H, Lippert TH. 2002. Estradiol metabolism and malignant disease. Maturitas 43: 1-10.  https://doi.org/10.1016/S0378-5122(02)00141-X
  45. Walle T. 2004. Absorption and metabolism of flavonoids. Free Rad. Biol. Med. 36: 829-837.  https://doi.org/10.1016/j.freeradbiomed.2004.01.002
  46. Gradolatto A, Canivenc-Lavier MC, Basly JP, Siess MH, Teyssier C. 2004. Metabolism of apigenin by rat liver phase I and phase II enzymes and by isolated perfused rat liver. Drug Metab. Dispos. 32: 58-65.  https://doi.org/10.1124/dmd.32.1.58
  47. Wen L, Jiang Y, Yang J, Zhao Y, Tian M, Yang B. 2017. Structure, bioactivity, and synthesis of methylated flavonoids. Ann. NY Acad. Sci. 1398: 120-129.  https://doi.org/10.1111/nyas.13350
  48. Tarui H, Abe J, Tomigahara Y, Kawamura S, Kaneko H. 2009. Metabolism of procymidone derivatives in female rats. J. Agric. Food Chem. 57: 10883-10888.  https://doi.org/10.1021/jf902006k
  49. Swan G. 1999. The pharmacology of halogenated salicylanilides and their anthelmintic use in animals. J. S. Afr. Vet. Assoc. 70: 61-70.  https://doi.org/10.4102/jsava.v70i2.756
  50. Deng P, You T, Chen X, Yuan T, Huang H, Zhong D. 2011. Identification of amiodarone metabolites in human bile by ultraperformance liquid chromatography/quadrupole time-of-flight mass spectrometry. Drug Metab. Dispos. 39: 1058-1069. https://doi.org/10.1124/dmd.110.037671