Gene expression profiling of human macrophages after graphene oxide and graphene nanoplatelets treatment reveals particle-specific regulation of pathways.

Graphene and its derivatives are attractive materials envisaged to enable a wealth of novel applications in many fields including energy, electronics, composite materials or health. A comprehensive understanding of the potential adverse effects of graphene-related materials (GRM) in humans is a prerequisite to the safe use of these promising materials. Here, we exploited gene expression profiling to identify transcriptional responses and toxicity pathways induced by graphene oxide (GO) and graphene nanoplatelets (GNP) in human macrophages. Primary human monocyte-derived macrophages (MDM) and a human macrophage cell line, i.e. differentiated THP-1 cells, were exposed to 5 or 20 µg/mL GO and GNP for 6 and 24 h to capture early and more persistent acute responses at realistic or slightly overdose concentrations. GO and GNP induced time-, dose- and macrophage type-specific differential expression of a substantial number of genes with some overlap between the two GRM types (up to 384 genes (9.6%) or 447 genes (20.4%) in THP-1 or MDM, respectively) but also a high number of genes exclusively deregulated from each material type. Furthermore, GRM responses on gene expression were highly different from those induced by inflammogenic material crystalline quartz (maximum of 64 (2.3%) or 318 (11.3%) common genes for MDM treated with 20 µg/mL GO and GNP, respectively). Further bioinformatics analysis revealed that GNP predominantly activated genes controlling inflammatory and apoptotic pathways whereas GO showed only limited inflammatory responses. Interestingly, both GRM affected the expression of genes related to antigen processing and presentation and in addition, GO activated pathways of neutrophil activation, degranulation and immunity in MDM. Overall, this study provides an extensive resource of potential toxicity mechanisms for future safety assessment of GRM in more advanced model systems to verify if the observed changes in gene expression in human macrophages could lead to long-term consequences on human health.

Structure-activity relationship of graphene-related materials: A meta-analysis based on mammalian in vitro toxicity data.

To support a safe application of graphene-related materials (GRMs) it is necessary to understand the potential negative impacts they could have on human health, in particular on the lung - one of the most sensitive exposure routes. Machine learning (ML) approaches can help analyse the results of multiple toxicity studies to understand the structure-activity relationship and the effect of experimental conditions, thus supporting predictive nanotoxicology. In this work we collected in vitro cytotoxicity data obtained from studies using lung cells; we then fitted multiple regression models to predict this endpoint based on the material properties and experimental conditions. Moreover, the data set was used to calculate the Benchmark Dose Lower Confidence Interval (BMDL), a dose descriptor widely used in risk assessment. Regression and classification models were applied for the prediction of the BMDL value and BMDL range. The analyses show that both cytotoxicity and the BMDL range can be predicted well (Q2 = 0.77 and accuracy = 0.71, respectively). Both physico-chemical characteristics such as the lateral size, number of layers, and functionalization, and experimental conditions such as the assay and media used were important predicting features, confirming the need for thorough characterization and reporting of these parameters.