Imidacloprid (IMI), a widely used neonicotinoid insecticide, has been associated with neurotoxic effects; however, the system-level mechanisms underlying these effects remain incompletely understood. Here, we integrated network toxicology with multi-omics analyses to investigate IMI-induced neurotoxicity in SH-SY5Y cells and the whole-organism model, Caenorhabditis elegans (C. elegans). Network toxicology identified 284 potential IMI-related targets, and protein-protein interaction network analysis further prioritized 45 core targets, including HSP90AA1, ESR1, MAPK3, SRC, MAPK1, IL6, BCL2, PRKACA, and MAPK8. Molecular docking suggested potential binding interactions between IMI and several core targets, while qRT-PCR provided transcript-level support for a subset of hub genes. Transcriptomic profiling revealed pronounced model-specific responses. In SH-SY5Y cells, IMI primarily induced neuron-related molecular alterations, characterized by disruption of voltage-gated calcium channel activity and enrichment of multiple synaptic pathways. In contrast, C. elegans exhibited broader organism-level transcriptomic remodeling involving developmental processes, extracellular structure organization, and stress-adaptive pathways, including the MAPK and FoxO signaling pathways. Untargeted metabolomics in SH-SY5Y cells further revealed biochemical remodeling related to the neuroactive ligand-receptor interaction pathway, glutathione metabolism, oxidative phosphorylation, and ABC transporter pathways. In addition, IMI significantly increased intracellular ROS levels and disrupted glutathione redox homeostasis, as reflected by altered GSH and GSSG levels and the GSH/GSSG ratio. Integrated analysis identified neuroactive ligand-receptor interaction and glutathione metabolism as shared pathway-level features across datasets, supporting a mechanistic model in which disruption of receptor-mediated neurotransmission is accompanied by redox imbalance. Overall, this study provides a systems-level view of IMI-induced neurotoxicity and highlights both shared pathway-level features and pronounced model-specific biological responses.
Hou et al. (Wed,) studied this question.