Laboratory for Metabolic Networks, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan.
Department of Cell and Biochemistry, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA.
J Nanobiotechnology. 2024 Oct 14;22(1):621. doi: 10.1186/s12951-024-02885-8.
Nanoparticles are increasingly being used in medicine, cosmetics, food, and manufacturing. However, potential toxicity may limit the use of newly engineered nanoparticles. Prior studies have identified particle characteristics that are predictive of toxicity, although the mechanisms responsible for toxicity remain largely unknown. Nanoparticle treatment in cell culture, combined with high-throughput chemical screen allows for systematic perturbations of thousands of molecular targets against potential pathways of toxicity. The resulting data matrix, called chemical compendium, can provide insights into the mechanism of toxicity as well as help classify nanoparticles based on toxicity pathway.
We performed unbiased screens of 1280 bioactive chemicals against a panel of four particles, searching for inhibitors of macrophage toxicity. Our hit compounds clustered upon inhibitors of kinases involved in phagocytosis, including focal adhesion kinase (FAK), with varying specificity depending on particles. Interestingly, known inhibitors of cell death including NLRP3 inflammasome inhibitor were unable to suppress particle-induced macrophage death for many of the particles. By searching for upstream receptors of kinases, we identified Cd11b as one of the receptors involved in recognizing a subset of particles. We subsequently used these hit compounds and antibodies to further characterize a larger panel of particles and identified hydrodynamic size as an important particle characteristic in Cd11b-mediated particle uptake and toxicity.
Our chemical compendium and workflow can be expanded across cell types and assays to characterize the toxicity mechanism of newly engineered nanoparticles. The data in their current form can also be analyzed to help design future high-throughput screening for nanoparticle toxicity.
纳米颗粒在医学、化妆品、食品和制造业中的应用越来越广泛。然而,潜在的毒性可能会限制新设计的纳米颗粒的使用。先前的研究已经确定了一些具有预测毒性的颗粒特征,尽管毒性的机制在很大程度上仍然未知。在细胞培养中进行纳米颗粒处理,并结合高通量化学筛选,可以系统地扰动数千个分子靶标,以寻找潜在的毒性途径。由此产生的称为化学文库的数据矩阵,可以深入了解毒性机制,并帮助根据毒性途径对纳米颗粒进行分类。
我们对 1280 种生物活性化合物进行了无偏筛选,针对四种颗粒进行了筛选,寻找巨噬细胞毒性的抑制剂。我们的命中化合物聚集在参与吞噬作用的激酶抑制剂上,包括粘着斑激酶(FAK),根据颗粒的不同而具有不同的特异性。有趣的是,包括 NLRP3 炎性体抑制剂在内的已知细胞死亡抑制剂不能抑制许多颗粒诱导的巨噬细胞死亡。通过寻找激酶的上游受体,我们确定了 Cd11b 是识别一组颗粒的受体之一。随后,我们使用这些命中化合物和抗体进一步表征了更大的颗粒面板,并确定了水动力粒径是 Cd11b 介导的颗粒摄取和毒性的重要颗粒特征。
我们的化学文库和工作流程可以扩展到细胞类型和测定中,以表征新设计的纳米颗粒的毒性机制。目前这些数据也可以进行分析,以帮助设计未来用于纳米颗粒毒性的高通量筛选。