COVID-19: Unravelling the changing immune environment of human lung cells in a search for biomarkers that are predictive for a cytokine storm

目前缺乏预测COVID-19感染严重程度和结果的模型和生物标志物。这样的生物标记物可以帮助预测预期需要的重症监护床位。重要的是，在实验室中很少或根本没有安全的模型，可以在不涉及人类志愿者或动物模型的情况下研究病毒的免疫进展，但在类人组织环境中对人类细胞可能产生的影响。COVID-19感染的严重程度与所谓的细胞因子风暴有关，这是一种过度产生的信号蛋白，用于免疫细胞到达感染部位，以应对肺部炎症。这会导致高度炎症，最终导致患者死亡。在细胞因子风暴的情况下，免疫系统开始破坏肺细胞，使呼吸困难。我们建议开发一种安全的实验模型，可以针对病毒的突变形式进行定制，以揭示COVID-19感染中人类肺细胞免疫环境的变化，从而寻找与感染相关的生物标志物。该模型将使人体测试能够预测感染进入细胞因子风暴的进展。

There is a lack of models and biomarkers to predict the severity and outcome of a COVID-19 infection. Such biomarkers could help in predicting the expected intensive care beds that are needed. Importantly, there are few or no models to use in the laboratory that are safe, that can study the immunologic progression of viruses in a condition that does not involve human volunteers or animal models but demonstrates possible effects in human cells in a human-like tissue environment.Severity in COVID-19 infection is related to the so-called cytokine storm, an excessive production of signalling proteins to immune cells to come to the site of infection in response to an inflammation in the lungs. This leads to hyper-inflammation which could eventually kill a patient. In case of a cytokine storm, the immune system begins to destroy lung cells and makes breathing difficult.We propose to develop a safe experimental model that can be customised to mutated forms of the virus to unravel the changing immune environment of human lung cells in COVID-19 infection in a search for biomarkers associated with the infection. The model will enable tests of people to predict the progression of an infection into a cytokine storm.