Back

Role of Nutritional Status on Arsenic Toxicity in Daphnia pulex: A Transcriptomic Perspective on Individual and Interactive Effects

DeTemple, E. R.; Jackson, C. E.; Schultz, A.; Hampton, T. H.; Shaw, J. R.; Chowdhury, P. R.

2026-08-19 pharmacology and toxicology
10.64898/2026.08.11.744190 bioRxiv
Show abstract

Inorganic arsenic is a widespread environmental contaminant and known human carcinogen, yet the mechanisms by which nutritional status modulates arsenic toxicity remain poorly understood. Here, we investigated the main and interactive effects of environmentally relevant concentrations of arsenic, low food quantity, and low dietary phosphorus supply on genome-wide gene expression in aquatic grazer Daphnia pulex. Differential gene expression analysis identified a total of 1,213 differently expressed genes with interactions of arsenic x nutrient stressors accounting for approximately 70% of the transcriptomic response. Low phosphorus emerged as a dominant main effect stressor and it also had a profound impact on transcription as a co-stressor. The low phosphorus x arsenic interaction exhibited the greatest transcriptional impact (435 DE genes), revealing that phosphorus limitation rather than food quantity influences arsenic toxicity at the gene expression level. Gene ontology and Pathway Activation Analysis revealed that main effects elicited simple yet distinct functional responses, whereas arsenic x nutrient interactions induced complex pathway-level disruptions including cell signaling, detoxification metabolism, DNA repair mechanisms, and energy homeostasis. Further assessment of gene expression revealed that all arsenic x nutrient interactions are antagonistic supporting previous literature that found arsenic behaves antagonistically as a co-stressor. Our results provide mechanistic insight into how nutritional status modulates arsenic toxicity and highlights the importance of considering arsenic x nutrient co-stressor interactions.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.