Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.06.27.734946

The effects of estrogen exposure on survival, growth, and fecundity of Daphnia magna

Abstract

High concentrations of steroidal hormone compounds are a growing source of concern for environmental pollution in aquatic ecosystems. In this study, we examine the effects of two estrogenic compounds (estriol and 17-ethinylestradiol) on fitness traits in the aquatic microcrustacean, Daphnia magna, a key bioindicator species for toxicology studies. The impacts were compared of two forms representing a natural and synthetic estrogenic compound. Growth and reproduction traits were assayed by exposing Daphnia to each estrogen type at four concentrations reflecting potential environmental exposure conditions up to acute toxicity levels (ranging from 0.1 - 50 {micro}g/L). Assaying the effects at a variety of concentrations is important given that it is known that hormone exposures can often result in non-monotonic responses. Both forms of estrogen impact a subset of the traits assessed, in some cases leading to beneficial changes and others causing harm. Estriol, the naturally-occurring estrogen, and EE2, the synthetic version, at high doses shift fitness traits in opposite directions such as adult growth rate as do at low doses for fecundity. In conclusion, our results support the need to assay a wide array of traits using multiple forms of steroidal hormones at a range of doses in order to assess non-monotonic patterns and their impact on an organismal fitness. In particular, assays that extend beyond the conventional measurements of lethality during acute exposure windows will be essential for understanding the impact of increased levels of hormone pollution on aquatic organisms and ecosystem health.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Boyle, S., Schaack, S.. 2026-07-02. The effects of estrogen exposure on survival, growth, and fecundity of Daphnia magna. https://doi.org/10.64898/2026.06.27.734946

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Translational Pharmacokinetics and Pharmacodynamics of a Cationic mRNA-Lipid Nanoparticle from Mice to Non-Human Primates

Cationic lipid nanoparticles have demonstrated unique potential for extrahepatic mRNA delivery, particularly enabling selective targeting of the pulmonary endothelium. However, their translational development has been hampered by reports of infusion-related immune reactions and innate immune system activation, most notably transient complement activation. Here, we present a case study illustrating the discovery and translational advancement of a selected cationic LNP into non-human primates (NHPs) for initial pharmacokinetic assessment and evaluation of potential immunostimulatory side effects. We show surface charge dependent organ-selective expression of reporter mRNAs from different LNPs in vivo. An mRNA encoding the Tie2 agonist COMP-Angl, was formulated with LNP002, and respective pharmacokinetic and pharmacodynamic readouts were analyzed in two independent non-human primate studies. Notably, dose-dependent transient complement activation could be abrogated by extending the infusion time. Finally, we identified the blood-borne pharmacodynamic biomarker PDGFB for LNP002/mRNA-76 treatment reflecting activated Tie2-signalling in healthy pulmonary endothelium in vivo supported by single cell sequencing and cluster-alignment of downstream effector genes with the same spatial profile as the delivered mRNA.

pharmacology and toxicology↗

Cytotoxic Effects of Multiple Pesticides and their Mixtures on Caco-2 Cells Evaluated by Using MTT and Trypan Blue Assays

BACKGROUND: Pesticides are extensively used in agriculture, raising concerns about their potential impact on human health through dietary and environmental exposure. OBJECTIVES: This study evaluated the in vitro cytotoxicity of ten commonly used pesticides and their mixtures (lambda-cyhalothrin, cypermethrin, deltamethrin, tebuconazole, glyphosate, acetamiprid, cyprodinil, piperonyl butoxide, fluopyram, and imazalil) on human intestinal Caco-2 cells. METHODS: Cytotoxicity was assessed using the MTT assay, as a measure of metabolic activity, and the trypan blue exclusion test, as an indicator of cell membrane integrity. FINDINGS: Results showed that high concentrations (100 mg/L) of all pesticides significantly reduced cell viability and vitality. Notably, glyphosate and tebuconazole exhibited significant toxicity even at lower concentrations, respectively 0.1 mg/L and 10 mg/L. Combination treatments (Top 3 and Top 8 pesticide mixtures) retained the cytotoxic effects observed for individual compounds, showing additive (non-synergistic) effects. CONCLUSIONS: Overall, these findings indicate that certain pesticides-based herbicides can exert cytotoxic effects on intestinal cells even at relatively low concentrations and highlight the importance of using the component-based approach in mixture risk assessment for humans. This study was performed as part of the EU SPRINT (Sustainable Plant Protection Transition: A Global Health Approach) project.

pharmacology and toxicology↗

Assessing chemical toxicity across Eukaryota using multimodal transformers

Biodiversity is globally threatened by chemical pollution, yet toxicity data remain unavailable for millions of species and tens of thousands of chemicals, severely limiting our ability to assess ecological impacts. Here we present TRIDENT-2, a multimodal artificial intelligence model for predicting chemical toxicity across evolutionarily diverse eukaryotic species. Trained on 560,780 toxicity assays spanning 82,775 chemicals, 6,793 species, and multiple exposure scenarios, TRIDENT-2 accurately predicts toxicity across Eukaryota with an average median absolute error ranging from 1.76 to 3.80. By jointly learning from chemical, biological, and experimental information, it remains accurate across broad chemical and taxonomic distances, allowing for toxicity assessment for species and chemicals beyond the current experimental evidence. Our findings demonstrate that artificial intelligence can help overcome longstanding data limitations in ecotoxicology, paving the way for improved decision-making and reducing chemical impacts on biodiversity and ecosystems.

pharmacology and toxicology↗