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Transcriptomics and metabolomics analyses of graphene oxide toxicity on porcine alveolar macrophages
Shanshen Gu1, Fan Lu1, Zhongcheng Gao1
1Key Laboratory for Animal Genetics, Breeding, Reproduction and Molecular Design, College of Animal Science and Technology, Yangzhou University, Yangzhou, China.
Abstract:
Graphene oxide (GO) is a type of nanomaterial widely used in tissue engineering, photocatalysis, and biomedicine. GO has been found to produce adverse effects on a broad range of cells and tissues. However, the molecular mechanisms underlying GO toxicity still remain to be explored. In this study, using porcine alveolar macrophages as a study model, we explored the toxic effects of GO and performed genome-wide detection of genes and metabolites associated with GO exposure using RNA-seq and liquid chromatograph mass spectrometer techniques. GO exposure significantly inhibited cell viability and induced apoptosis and oxidative stress in porcine alveolar macrophages. Further, GO exposure promoted cellular inflammation by upregulating the expression of pro-inflammatory cytokines (IL-6, IL-8, and IL-12). Transcriptomic analysis of GO-exposed cells revealed 424 differentially expressed genes. Functional enrichment analysis showed that the differentially expressed genes were significantly enriched in the pathways of Ribosome and oxidative phosphorylation (OXPHOS). In addition, metabolic analysis identified 203 differential metabolites, and these metabolites were significantly enriched in biosynthesis of cofactors, purine metabolism, and nucleotide metabolism. Integrative analyses of transcriptome and metabolome showed that OXPHOS was the most significantly enriched pathway and the involved genes were downregulated. This study revealed the toxic effects of GO on porcine alveolar macrophages and provided global insights to the metabolomic and transcriptomic alterations related to GO exposure. The results contributed to our understanding of the molecular mechanism of GO, and may further promote the detection of biomarkers for the prediction and control of GO toxicity.

