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1 Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695-7566, USA
2 The Hamner Institutes for Health Sciences, CIIT Centers for Health Research, Division of Computational Biology, 6 Davis Drive, PO Box 12137, Research Triangle Park, North Carolina 27709-2137, USA
(Requests for offprints should be addressed to Q Zhang; Email: qzhang{at}thehamner.org)
Steroid hormone receptors are the targets of many environmental endocrine active chemicals (EACs) and synthetic drugs used in hormone therapy. While most of these chemical compounds have a unidirectional and monotonic effect, certain EACs can display non-monotonic doseresponse behaviors and some synthetic drugs are selective endocrine modulators. Mechanisms underlying these complex endocrine behaviors have not been fully understood. By formulating an ordinary differential equation-based computational model, we investigated in this study the steady-state doseresponse behavior of exogenous steroid ligands in an endogenous hormonal background under various parameter conditions. Our simulation revealed that non-monotonic doseresponses in gene expression can arise within the classical genomic framework of steroid signaling. Specifically, when the exogenous ligand is an agonist, a U-shaped doseresponse appears as a result of the inherently nonlinear process of receptor homodimerization. This U-shaped doseresponse curve can be further modulated by mixed-ligand heterodimers formed between endogenous ligand-bound and exogenous ligand-bound receptor monomers. When the heterodimer is transcriptionally inactive or repressive, the magnitude of U-shape increases; conversely, when the heterodimer is transcriptionally active, the magnitude of U-shape decreases. Additionally, we found that an inverted U-shaped doseresponse can arise when the heterodimer is a strong transcription activator regardless of whether the exogenous ligand is an agonist or antagonist. Our work provides a novel mechanism for non-monotonic, particularly U-shaped, doseresponse behaviors observed with certain steroid mimics, and may help not only understand how selective steroid receptor modulators work but also improve risk assessment for EACs.
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