IntroductionNanotechnology, the manipulation and control of matter at the nanometer scale (10−9), has revolutionized various fields by enabling the development of materials with unique physical and chemical properties1,2. Among these, silver nanoparticles (AgNPs) have gained considerable attention due to their distinctive antimicrobial, optical, and electrical properties3,4,5, making them highly valuable in medical applications3, environmental systems6, and industrial processes. Despite these advantages, increasing concerns have emerged regarding their potential adverse effects on biological systems, particularly their toxicity. Several studies have demonstrated that AgNPs can generate reactive oxygen species (ROS), leading to oxidative stress, DNA damage, and apoptosis7,8,9,10,11,12,13. Furthermore, prolonged exposure to AgNPs has been associated with respiratory disorders in humans, highlighting the potential risks to human health14.However, the precise molecular mechanisms driving these effects are still insufficiently understood, especially the temporal evolution of these toxicological responses15,16. While previous research has established that AgNPs can cause oxidative stress and disrupt key cellular functions such as cytoskeletal stability17, and mitochondrial energy production18, these studies have largely focused on static endpoints, capturing only a snapshot of cellular changes. Although research has identified AgNPs-induced ROS generation and subsequent cell death19,20,21, it remains unclear how these processes unfold over time and how different molecular pathways are sequentially activated or inhibited. Understanding the temporal progression of cellular responses is essential to fully grasp the mechanisms of toxicity. This knowledge enables the identification of critical time points for targeted safety interventions to predict and mitigate risks.The primary objective of this study is to investigate the temporal changes in gene expression under AgNPs exposure and to elucidate the molecular mechanisms of AgNPs-induced toxicity. By examining these changes over time, we aim to provide a dynamic view of how AgNPs interact with biological systems at the molecular level. Specifically, we will focus on identifying key transcriptomes and pathways involved in oxidative stress, endoplasmic reticulum (ER) stress, DNA damage, and apoptosis, which are crucial to the toxicological effects of AgNPs. In doing so, we aim to bridge the gap between static molecular observations and the dynamic processes that regulate nanoparticle-induced cellular stress.To achieve these goals, we utilized publicly available transcriptomic datasets containing more than two time points to identify differentially expressed genes (DEGs) in response to AgNPs. This will allow us to capture the molecular changes at various time points, offering insights into the sequence of gene activation or inhibition. Additionally, we constructed protein-protein interaction (PPI) networks using the Search Tool for the Retrieval of Interacting Proteins (STRING)22 to determine critical hub genes and pathways affected by nanoparticle-induced stress. STRING is particularly well-suited for this analysis, as it integrates known and predicted protein interactions, allowing us to map out key molecular networks. Furthermore, we used CellDesigner23,24 to visualize these interactions and COPASI25 to estimate rate constants from rate equations and perform dynamic simulations. It provides a comprehensive view of how gene expression evolves over time under AgNPs-induced stress, enabling us to explore both static molecular interactions and dynamic processes.The significance of this research lies in its potential to advance the understanding of nanotoxicology by providing a detailed temporal framework for nanoparticle-induced molecular disturbances. By tracking how gene and protein expression changes over time, this study aims to contribute to more accurate safety assessments and predictive models for the use of nanoparticles in medical and industrial applications. Ultimately, the insights gained from this research could guide the development of safer nanomaterials and inform regulatory guidelines for their use.ResultsIdentification of DEGs in response to AgNPs exposureTo assess the cellular response to AgNPs over time, transcriptomic analyses were conducted at multiple time points post-exposure. Figure 1a shows a Principal Component Analysis (PCA) plot of gene expression profiles in treated and control cells at 1, 6, and 24 h post-exposure. The PCA indicated distinct clustering patterns between treated and control samples, with clear separation of time points along the principal components. PC1 and PC2 accounted for 23.2% and 17.7% of the variance, respectively, illustrating a temporal shift in gene expression following AgNPs treatment. To further confirm the robustness of these clustering patterns, we applied t-SNE and UMAP for dimensionality reduction, both of which reproduced the clear separation between treated and control samples initially observed in the PCA (Supplementary Fig. S1a, b).Fig. 1: Impact of AgNPs treatment on cellular responses over time.a Principal component analysis (PCA) of gene expression profiles (four biological replicates each; for treated group of 6 h and 24 h, three biological replicates each). PCA plot showing the clustering of gene expression profiles from cells treated with AgNPs and untreated controls at different time points (1 h, 6 h, and 24 h). Each point represents an individual sample, with samples grouped by treatment and time point as indicated by color. b Venn diagram of differentially expressed genes (DEGs). Venn diagram displays the overlap of DEGs across three time points (1 h, 6 h, and 24 h) following AgNPs treatment. Each circle represents the set of DEGs at a specific time point, highlighting both unique and common gene expression responses over time. DEGs were labeled as genes for which the Benjamini–Hochberg adjusted p-value was less than 0.05. c Gene ontology (GO) enrichment analysis of upregulated and downregulated genes at 6 h and 24 h following AgNPs treatment. Dot plots depicting the GO terms enriched among upregulated (left columns in each plot) and downregulated (right columns in each plot) genes at 6 h (left panel) and 24 h (right panel) post-treatment with AgNPs. Each dot represents a GO term, with the color indicating the adjusted p-value (red signifies higher significance) and the dot size corresponding to the number of genes associated with each term (larger dots represent a greater gene count). Benjamini–Hochberg adjusted p-values