Chronic Inflammation Prediction for Inhaled Particles, the Impact of Material Cycling and Quarantining in the Lung Epithelium

We are daily exposed to a multitude of health hazardous airborne particulate matter with notable deposition in the fragile alveolar region of our lungs. Hence, there is a great need for identification and prediction of material-associated diseases, currently hindered due to the lack of in-depth understanding of causal relationships, in particular between acute exposures and chronic symptoms. By applying advanced microscopies and omics to in vitro and in vivo systems, together with in silico molecular modelling, we have here determined that the long-lasting response to a single exposure can originate from the interplay between the newly discovered nanomaterial quarantining and nanomaterial cycling between different lung cell types. This new insight finally allows us to predict the spectrum of lung inflammation associated with materials of interest using only in vitro measurements and in silico modelling potentially relating outcomes to material properties for large number of materials thus boosting safe-by-design-based material development. Because of its profound implications for animal-free predictive toxicology, our work paves the way to a more efficient and hazard-free introduction of numerous new advanced materials into our lives. We performed gene expression microarray analysis of LA-4 cells, MH-S cells or cocultures treated with TiO2 nanotubes (for 4h or 48h) or controls (for 0h or 48h)

人类每日都会接触大量有害健康的空气传播颗粒物，这些颗粒物会在肺部脆弱的肺泡区域发生显著沉积。因此，亟需识别并预测与材料相关的疾病，但目前由于对因果关系（尤其是急性暴露与慢性症状之间的因果关系）缺乏深入认知，这一目标难以实现。我们通过将先进显微技术、组学（omics）应用于体外（in vitro）与体内（in vivo）实验体系，并结合计算机（in silico）分子建模，最终明确：单次暴露所引发的长期生物学效应，可源于新发现的纳米材料隔离作用与不同肺细胞类型间纳米材料循环转运之间的相互作用。这一全新发现使得我们仅需借助体外实验检测与计算机分子建模，即可预测目标材料相关的肺部炎症谱；该方法可将实验结果与大量材料的固有属性相关联，进而推动基于安全设计（safe-by-design）理念的材料研发进程。鉴于该研究对无动物替代预测毒理学（animal-free predictive toxicology）具有重要意义，本研究为大量新型先进材料高效、无害地融入人类生活铺平了道路。本研究对经二氧化钛（TiO₂）纳米管处理4小时或48小时的LA-4细胞、MH-S细胞及其共培养体系，以及经对照处理0小时或48小时的上述样本进行了基因表达微阵列分析。