Binding interaction of typical emerging contaminants on Gobiocypris rarus transthyretin: an in vitro and in silico study

doi:10.1007/s11783-024-1895-1

2024, Vol. 18

Issue (11) : 135 https://doi.org/10.1007/s11783-024-1895-1

Binding interaction of typical emerging contaminants on Gobiocypris rarus transthyretin: an in vitro and in silico study

Xiangqiao Li¹, Huihui Liu¹, Songshan Zhao¹, Peter Watson², Xianhai Yang¹(

)

¹. Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
². Los Alamos National Laboratory, New Mexico Los Alamos, NM 87545, USA

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Abstract

● Potential binding potency of 29 ECs on Gobiocypris rarus transthyretin were tested.

● The Gobiocypris rarus TTR binding affinity of 3 ECs was higher than that of T₄.

● High throughput screening classification models for fish and human TTR were derived.

● “TTR Profiler” can predict the potential fish and human TTR disrupting effects data.

Emerging contaminants (ECs) have drawn global concern, and the endocrine disrupting chemicals is one of the highly interested ECs categories. However, numerous ECs lacks the basic information about whether they can disturb the endocrine related biomacromolecules or elicit endocrine related detrimental effects on organism. In this study, the potential binding affinity and underlying binding mechanism between 29 ECs from 7 chemical groups and Gobiocypris rarus transthyretin (CrmTTR) are investigated and probed using in vitro and in silico methods. The experimental results demonstrate that 14 selected ECs (11 disinfection byproducts, 1 pharmaceuticals and personal care product, 1 alkylphenol, 1 perfluoroalkyl and polyfluoroalkyl substance) are potential CrmTTR binders. The CrmTTR binding affinity of three ECs (i.e., 2,6-diiodo-4-nitrophenol (logRP(T₄) = 0.678 ± 0.198), 2-bromo-6-chloro-4-nitrophenol (logRP(T₄) = 0.399 ± 0.0908), tetrachloro-1,4-benzoquinone (logRP(T₄) = 0.272 ± 0.0655)) were higher than that of 3,3′,5,5′-tetraiodo-L-thyronine, highlighting that more work should be performed to reveal their potential endocrine related harmful effects on Gobiocypris rarus. Molecular docking results imply that hydrogen bond and hydrophobic interactions are the dominated non-covalent interactions between the active disruptors and CrmTTR. The optimum mechanism-based (for CrmTTR), and high throughput screening (for CrmTTR, little skate-TTR, seabream-TTR, and human-TTR) binary classification models are developed using three machine learning algorithms, and all the models have good classification performance. To facilitate the use of developed high throughput screening models, a tool named “TTR Profiler” is derived, which could be employed to determine whether a given substance is a potential CrmTTR, little skate-TTR, seabream-TTR, or human-TTR disruptor or not.

Keywords Endocrine disrupting effects Hormone transporter Endocrine disrupting chemicals Disinfection byproducts Classification model

Corresponding Author(s): Xianhai Yang

Issue Date: 11 September 2024

Cite this article:

Xiangqiao Li,Huihui Liu,Songshan Zhao, et al. Binding interaction of typical emerging contaminants on Gobiocypris rarus transthyretin: an in vitro and in silico study[J]. Front. Environ. Sci. Eng., 2024, 18(11): 135.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1895-1
https://academic.hep.com.cn/fese/EN/Y2024/V18/I11/135

Chemical groups	Chemical name	IC₅₀ (nmol/L)	logRP(T₄)	Species	Reference
Thyroid hormones	3,3′,5-Triiodo-L-thyronine	415±64.5	0.0946±0.0805	Gobiocypris rarus	This study
Thyroid hormones	3,3′,5,5′-Tetraiodo-L-thyronine	516±52.2	0	Gobiocypris rarus	This study
Disinfection byproducts	3,5-Diiodo-2-hydroxybenzaldehyde	1863±118	0.558±0.052	Gobiocypris rarus	This study
	2,6-Dibromo-p-benzoquinone	994±92.9	0.285±0.0598
	2,6-Dichloro-p-benzoquinone	1049±102	0.308±0.0609
	Bromochlorophenol Blue	75393±44420	2.16±0.2596
	2-Bromo-6-chloro-4-nitrophenol	206±37.7	0.399±0.0908
	2,5-Dibromo-p-benzoquinone	839±56.4	0.211±0.0527
	2,6-Diiodo-4-nitrophenol	109±48.4	0.678±0.198
	3,5-Diiodo-4-hydroxybenzaldehyde	542±34.8	0.021±0.052
	Tetrachloro-1,4-benzoquinone	276±30.9	0.272±0.0655
	3-Methyl-2-nitrophenol	201281±80522	2.59±0.179
	N,N-Diphenylnitrous amide	>10000 (Inactive)
	4-Bromo-2,6-ditert-butylphenol	>10000 (Inactive)
	3,5-Dichlorobisphenol A	1006±51.9	0.290±0.0493	Gobiocypris rarus	This study
		19	0.0734^a)	Gallus gallus	Yamauchi et al. (2003)
		44.7	1.49^a)	Rana catesbeiana	Yamauchi et al. 2003
Pesticides	Flupyradifurone	>10000 (Inactive)		Gobiocypris rarus	This study
	Imidacloprid	>10000 (Inactive)
Pharmaceuticals and personal care products	Triclosan	1191±154	0.363±0.0712	Gobiocypris rarus	This study
		4179	1.82	Human	Weiss et al. (2015)
		669	1.52	Human	Marchesini et al. (2008)
	Triclocarban	>10000 (Inactive)		Gobiocypris rarus	This study
	Triclocarban	Inactive		Human	Weiss et al. (2015)
	Methylparaben	>10000 (Inactive)		Gobiocypris rarus	This study
	Methylparaben	Inactive		Human	Zhang et al. (2015)
Alkylphenols	Nonylphenol	10980±3974	1.33±0.163	Gobiocypris rarus	This study
		10000	2.10	Human	Weiss et al. (2015)
		Active		Human	Collet et al. (2020)
		1330	1.77^b)	Gallus gallus	Yamauchi et al. (2003)
		2730	3.28^b)	Rana catesbeiana	Yamauchi et al. (2003)
		1000	2.01^b)	Coturnix japonica	Ishihara et al. (2003)
		Inactive		Leucoraja erinacea	Suzuki et al. (2015)
		17035	2.91	Sparus aurata	Morgado et al. (2007)
Perfluoroalkyl and polyfluoroalkyl substances	6:2 Fluorotelomer sulfonic acid	64417±6696	2.10±0.0630	Gobiocypris rarus	This study
		Inactive		Human	Simon et al. (2011)
		12000	2.20	Human	Langberg et al. (2024)
Plasticizers	Benzyl butyl phthalate	>10000 (Inactive)		Gobiocypris rarus	This study
		Inactive		Human	Weiss et al. (2015)
		Inactive		Leucorajaerinacea	Suzuki et al. (2015)
		2400	2.39^b)	Coturnix japonica	Ishihara et al. (2003)
Tire additives and their derivatives	N-(1,3-Dimethylbutyl)-N′-phenyl-p-phenylenediamine	>10000 (Inactive)		Gobiocypris rarus	This study
	N-(1,3-Dimethylbutyl)-N′-phenyl-p-Phenylenediamine quinone	>10000 (Inactive)
	2-Aminobenzothiazole	>100000 (Inactive)
	2-Hydroxybenzothiazole	>10000 (Inactive)
	N,N-Diphenylguanidine	>10000 (Inactive)
	N,N -Diphenylurea	>10000 (Inactive)
	4-Hydroxydiphenylamine	>10000 (Inactive)
	Hexa(methoxymethyl) melamine	>100000 (Inactive)

Tab.1 Information of model compounds and their values of IC₅₀ and logRP(T₄) ^a)

Fig.1 Fluorescence displacement curves of 3,3′,5,5′-tetraiodo-lthyronine (T₄), 3,3′,5-tetraiodo- L-thyronine (T₃), 2,6-diiodo-4-nitrophenol (2,6-DINP), 2-bromo-6-chloro-4-nitrophenol (2,6-BCNP), 3,5-diiodo-4-hydroxybenzaldehyde (4-DIHBA), 2,6-dichloro-p-benzoquinone (2,6-DCBQ), 2,6-dibromo-p-benzoquinone (2,6-DBBQ), 3-methyl-2-nitrophenol (3-MNP), 3,5-dichlorobisphenol A (3,5-DCBPA), tetrachloro-1,4-benzoquinone (TCBQ), triclosan (TCS), nonylphenol (NP), 6:2 fluorotelomer sulfonic acid (6:2-FTSA), 3,5-diiodo-2-hydroxybenzaldehyde (2-DIHBA), imidacloprid (IMI), benzyl butyl phthalate (BBP), and N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine quinone (6PPDQ) titrated into the solution of ANSA (50 μmol/L) and recombinant CrmTTR (0.5 μmol/L). The error bars represent the standard deviation of triplicate independent determinations.

Fig.2 Classification of the binding potency of chemicals to recombinant CrmTTR. Abbreviations: 3,5-diiodo-2-hydroxybenzaldehyde (2-DIHBA), triclosan (TCS), 2,6-dichloro-p-benzoquinone (2,6-DCBQ), 3,5-dichlorobisphenol A (3,5-DCBPA), 2,6-dibromo-p-benzoquinone (2,6-DBBQ), 2,5-dibromo-p-benzoquinone (2,5-DBBQ), 3,5-diiodo-4-hydroxybenzaldehyde (4-DIHBA), tetrachloro-1,4-benzoquinone (TCBQ), 2-bromo-6-chloro-4-nitrophenol (2,6-BCNP), 2,6-diiodo-4-nitrophenol (2,6-DINP), nonylphenol (NP), 6:2 fluorotelomer sulfonic acid (6:2-FTSA), Bromochlorophenol Blue (BCPB), 3-methyl-2-nitrophenol (3-MNP). The logRP(T₄) value of low, moderate and high potency CrmTTR binder was < −2.26, −2.26 – −1.26, > −1.26, respectively (Hamers et al., 2006; Zhao et al., 2023). The error bars represent the standard deviation of triplicate independent determinations. Structures within the figure are the three compounds with logRP(T₄) > 0.

Fig.3 Binding mode of 2,6-diiodo-4-nitrophenol (A), 2-bromo-6-chloro-4-nitrophenol (B), tetrachloro-1,4-benzoquinone (C) and 3,5-diiodo-4-hydroxybenzaldehyde (D) in CrmTTR. The green dashed lines represent hydrogen bonds. Binding mode was illustrated using Pymol v2.6.0 software (The PyMOL Molecular Graphics System, 2024).

Tab.2 Statistical parameters of optimum binary classification models based on gradient boosting decision tree

Fig.4 SHAP summary plot for the human TTR high throughput screening binary classification models based on gradient boosting decision tree (A) and importance of the five predictive variables in the gradient boosting decision tree model (B).

Fig.5 Application domain of gradient boosting decision tree models for Gobiocypris rarus (A) and human (B) TTR high throughput model defined by using Euclidean distance vs leverage-based methods.

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