MICA_Multimodal analysis of the pathophysiology of bronchiectasis

支气管扩张是一种由于持续或复发性炎症而损害肺部的疾病。支气管扩张症患者会持续从胸部产生痰或粘液（称为“痰”），这是细菌持续感染和炎症引起的结果。虽然有一些已知的原因支气管扩张症（例如囊性纤维化，其中痰非常粘并且不能被带出，或者原发性纤毛运动障碍，其中气道内部的毛发不起作用并且不能移动痰），在英国患有支气管扩张症的20万或更多人中的大多数没有已知的病因，这被称为“特发性支气管扩张症”。目前没有针对这种情况的许可药物，因此，对受影响个体的治疗基于抗生素治疗和物理治疗（以清除痰液）。因此，随着肺损伤随时间推移而进展，患者通常会反复出现临床恶化（需要在家或住院进行抗生素治疗）、严重疲劳和呼吸困难恶化。我们目前还不知道是什么原因导致支气管扩张的发展，或者炎症涉及哪些途径，我们也没有一个很好的模型系统来测试这种疾病的新药。因此，我们的目标是将联合收割机对支气管扩张症（以及持续性咳嗽或支气管炎等相关疾病）患者的大量不同DNA样本的遗传分析与基于人工智能的DNA变异对细胞行为方式的影响预测相结合，以便列出可能增加特发性支气管扩张症风险的基因列表。同时，我们将观察从支气管扩张患者（以及同龄健康人）身上采集的样本，观察单个细胞对刺激的反应，从而开始了解支气管扩张中的炎症类型（及其潜在原因）。然后，我们将联合收割机结合这两种不同方法的发现，以创建支气管扩张是如何引起的模型，然后测试我们的想法，看看引入特定DNA变异的影响（使用基因编辑）和特定类型的炎症损伤的影响，在实验室中特定细胞类型的行为。最后，我们将尝试在实验室中重建肺部环境，通过使用一个系统，我们将所有重要的细胞培养在一起，并研究它们如何相互作用以及与细菌相互作用。然后，我们可以添加特定的炎症损伤或引入我们在患者中看到的DNA变异，以了解肺部如何对它们做出反应。我们期望这种“芯片上的肺”模型将提供一个非常有用的工具来理解支气管扩张发生的原因并发现新的治疗方法。

Bronchiectasis is a disease that damages the lungs because of constant or recurrent inflammation. People with bronchiectasis will continuously produce phlegm or mucus from their chest (termed 'sputum'), as a consequence of persistent infection with bacteria and the inflammation this causes. While there are some known causes of bronchiectasis (such as cystic fibrosis, where the sputum is very sticking and cannot be brought up, or Primary ciliary dyskinesia, where there hairs the coat the inside of the airways don't work and cannot move the sputum), the majority of the 200,000 or more people who suffer with bronchiectasis in the UK have no known cause for there illness, which is referred to as 'idiopathic bronchiectasis'. There are currently no licensed medicines for this condition and, as a consequence, treatment of affected individuals is based around antibiotic treatment and physiotherapy (to clear the sputum). Patients will therefore often suffer from repeated episodes of clinical deterioration (requiring antibiotic treatment at home or in hospital), severe fatigue, and worsening breathlessness as the lung damage progresses over time. We currently do not understand what causes the development of bronchiectasis or what pathways are involved in the inflammation, and we do not have a good model system to test new drugs for this disease. Our aim therefore is to combine genetic analysis of a number of different DNA collections of people with bronchiectasis (and related conditions such as persistent cough or bronchitis) with artificial intellegence-based prediction of the effect of DNA variations on the way cells behave, in order to draw up a list of genes that may increase the risk of developing idiopathic bronchiectasis. At the same time, we will look at samples taken from patients with bronchiectasis (and healthy people of the same age) to look at the response of individual cells to stimuli and thus begin to understand the types of inflammation (and their potential causes) in bronchiectasis. We will then combine the findings of these two different approaches to create a model of how bronchiectasis is caused ad then test our ideas looking at the effect of introducing particular DNA variations (using gene editing) and the effect of particular types of inflammatory insults, on the behaviour of particular cell types in the lab. Finally we will try to re-create the lung environment in the lab by using a system where we grow all the important cells together and study how they interact with each other and with bacteria. We can then add particular inflammatory insults or introduce DNA variations we see in patients to understand how the lung might respond to them. We expect that this 'lung-on-a-chip' model will provide an very useful tool to understand why bronchiectasis occurs and to discover new treatments.