This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Chemicals affect biological species in different ways, posing a major challenge for assessing environmental chemical risks. However, the mechanisms behind these differences have remained largely unknown.
To investigate, a research team from Ehime University focused on estrogen receptor alpha (ERα) and used "New Approach Methodologies" (NAMs)—a framework that combines genetic sequence analysis (bioinformatics), computer-based 3D structural modeling (in silico analysis), artificial intelligence (AI/machine learning) and cell-based experiments (in vitro assays). The approach revealed how ecological differences among mammals and amino acid variations in ERα influence their responses to chemicals. For their study published in Environmental Science & Technology, the researchers analyzed ERα sequences from 169 mammalian species and 107 ecological traits—such as diet, habitat and reproductive strategy—revealed distinct groupings aligned with the animals' lifestyles.
A 3D structural analysis of the ERα protein also showed that omnivores tend to have larger "binding pockets"—where chemicals fit into the receptor—than carnivores and herbivores. Cell-based experiments targeting ERα from six mammalian species demonstrated marked differences in sensitivity to natural and synthetic estrogenic chemicals. Notably, cetaceans (whales and dolphins) had a unique mutation: The 349th amino acid of ERα was serine rather than the asparagine found in human ERα.
This mutation alters the structure of ERα and is linked to aquatic adaptation and changes in chemical responsiveness. Model analysis powered by AI (machine learning) identified the "brain-to-body mass ratio" (the ratio of brain weight to total body weight) as a promising factor for predicting variations in ERα structure and differences in chemical sensitivity. The study identifies ecological and ERα structural factors that shape species-specific chemical sensitivities.
The findings offer insight into the molecular mechanisms needed to predict chemical risks to wildlife with high precision. Robledo et al, Ecological and Structural Drivers Underlying Species-Specific Estrogen Receptor α Responses to Estrogenic Chemicals in Mammals, Environmental Science & Technology (2026). DOI: 10.1021/acs.est.6c04974 Journal information: Environmental Science & Technology BA art history, MA material culture.
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