In Silico Analysis of the Interaction of Avian Aryl Hydrocarbon Receptors and Dioxins to Decipher Isoform-, Ligand-, and Species-Specific Activations

Abstract

The aryl hydrocarbon receptor (AHR) mediates toxic responses to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and other dioxin-like compounds (DLCs). Avian species possess multiple AHR isoforms (AHR1, AHR1β, and AHR2) that exhibit species- and isoform-specific responses to ligands. To account for the ligand preference in terms of the structural features of avian AHRs, we generated in silico homology models of the ligand-binding domain of avian AHRs based on holo human HIF-2α (PDB entry 3H7W ). Molecular docking simulations of TCDD and other DLCs with avian AHR1s and AHR2s using ASEDock indicated that the interaction energy increased with the number of substituted chlorine atoms in congeners, supporting AHR transactivation potencies and World Health Organization TCDD toxic equivalency factors of congeners. The potential interaction energies of an endogenous AHR ligand, 6-formylindolo [3,2-b] carbazole (FICZ) to avian AHRs were lower than those of TCDD, which was supported by a greater potency of FICZ for in vitro AHR-mediated transactivation than TCDD. The molecular dynamics simulation revealed that mean square displacements in Ile324 and Ser380 of TCDD-bound AHR1 of the chicken, the most sensitive species to TCDD, were smaller than those in other avian AHR1s, suggesting that the dynamic stability of these amino acid residues contribute to TCDD preference. For avian AHR2, the corresponding residues (Val/Ser or Val/Ala type) were not responsible for differential TCDD sensitivity. Application of the three-dimensional reference interaction site model showed that the stabilization of TCDD binding to avian AHRs may be due to the solvation effect depending on the characteristics of two amino acids corresponding to Ile324 and Ser380 in chicken AHR1. This study demonstrates that in silico simulations of AHRs and ligands could be used to predict isoform-, ligand-, and species-specific interactions.

Ecological factors drive natural selection pressure of avian aryl hydrocarbon receptor 1 genotypes

“…FICZ (6-formylindolo [3,2-b] carbazole) is a tryptophan metabolite and has a high affinity to AHR3132. Recent studies have shown that transactivation potencies of FICZ are greater than TCDD regardless of the avian AHR1 genotype2033. In addition, there are fewer inter-species differences in AHR1 sensitivity to this endogenous ligand33, whereas there are large differences in responses to TCDD of the AHR1 among species2024.…”

“…The TCDD-EC 50 values for AHR1-mediated transactivation were in the order of ck AHR1 (0.030 nM) < bfa AHR1 (0.077 nM) < cc AHR1 (0.36 nM) as expected from the AHR1 genotype. Furthermore, in silico docking simulations of avian AHR1 and TCDD interactions suggested that the thermodynamic stability of the two amino acid residues involved in the interaction with TCDD reflect the sensitivity to TCDD in these avian species 20 .…”

The tryptophan derivative 6-formylindolo[3,2-b]carbazole, FICZ, a dynamic mediator of endogenous aryl hydrocarbon receptor signaling, balances cell growth and differentiation

“…Work with recently developed crystal AHR structures (Schulte et al 2017;Seok et al 2017) have not yet revealed important FICZ-interacting residues but in studies using homology models of the AHR PAS domain several residues in the binding pocket that are critical for strong binding of FICZ have been revealed. The somewhat similar binding of TCDD and FICZ involves hydrogen bonding with residues Ser359 and Gln377 in the PASB region of murine AHR (equivalent to Ser365 and Gln383 of the human AHR) (Bisson et al 2009;Nuti et al 2014;Hirano et al 2015;Miyagi et al 2015).…”

Adverse Outcome Pathway on Aryl hydrogen receptor activation leading to early life stage mortality, via reduced VEGF

“…In silico homology modeling of the ligand binding domain of the AHR in combination with molecular docking simulations can provide valuable insight into the transactivationpotential of a diverse array of AHR ligands. Such models have been developed for multiple AHR isoforms and ligands (high/low affinity, endogenous and synthetic, agonists and antagonists), and can accurately predict ligand potency based on their structure and physicochemical properties (Bonati et al 2017;Hirano et al 2015;Sovadinova et al 2006).…”