Molecular profiling of immune dysregulation in acute exacerbations: from peanut allergy to COVID-19

有效的免疫反应对健康和福祉至关重要，但由于尚不清楚的原因，它们可能会变得失调，导致许多炎症情况，包括过敏和哮喘，以及在对可能导致败血症的感染(包括目前的SARS-CoV-2大流行)做出反应时观察到的危及生命的全身性炎症综合征。认识到急性调节失调的免疫反应对于确保及时和适当的治疗至关重要--然而，许多临床决策依赖于与某些实验室结果有关的主观体征和症状。另一种方法是开发分子表型工具，使用质谱学来提供详细的分子代谢和蛋白质组学特征。由此产生的丰富的数据集还可以支持诊断生物标记物的开发，并提供对疾病机制的见解，从而支持更有效的治疗和管理战略。利用两个不同的人群，一个是经历食物过敏反应导致的免疫反应失调的人群，另一个是在新冠肺炎感染期间经历严重免疫中断的个人。与工业合作伙伴Waters Corporation合作，这两个集团的血浆蛋白质组代谢组将使用最先进的离子迁移率质谱仪进行全面表征，它提供了允许对分子结构进行3D表征的数据。生物信息学分析将用于识别数据集中的蛋白质和代谢物，并应用途径分析等技术。生成的高维数据集还将用于使用机器学习方法和贝叶斯推理识别临床表型的蛋白质组和代谢组标记，以支持快速临床决策和识别疾病路径，这将有助于识别新的治疗方法和管理策略。

An effective immune response is essential to health and well-being but, for reasons that are not well understood, they can become dysregulated causing numerous inflammatory conditions including allergies and asthma as well as life-threatening systemic inflammatory syndromes observed in response to infections which can result in sepsis, including the current pandemic SARS-CoV-2. Recognition of acute dysregulated immune responses is essential to ensure prompt and appropriate treatment - yet much of clinical decision making relies on subjective signs and symptoms linked with certain laboratory findings. An alternative approach is to develop molecular phenotyping tools using mass spectrometry to provide detailed molecular metabolic and proteomic profiles. The resulting rich data sets can also support development of diagnostic biomarkers as well as providing insights into disease mechanisms which can support more effective therapeutic and management strategies. Capitalising on two contrasting populations experiencing a dysregulated immune responses resulting from a food allergic reaction and individuals experiencing severe immune disruption during a COVID-19 infection. Working with the industrial partner, Waters Corporation, the plasma proteome metabolome of the two groups will be comprehensively characterised using state-of-the art ion mobility mass spectrometry which provides data that allows the 3D structural characterisation of molecules. Bioinformatic analysis will be used to identify proteins and metabolites in the data sets and apply techniques such as pathway analysis. The resulting high dimensional data sets will also be used to identify proteomic and metabolomic markers of clinical phenotypes using machine learning methods and Bayesian inference to support rapid clinical decision making and identify disease pathways which will help support identification of new treatments and management strategies.