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，它们可能变得失调，导致许多炎症状况，包括过敏和哮喘，以及在感染反应中观察到的危及生命的全身性炎症综合征，这些感染可能导致败血症，包括当前的大流行SARS-CoV-2。识别急性失调的免疫反应对于确保及时和适当的治疗至关重要，但许多临床决策依赖于与某些实验室结果相关的主观体征和症状。另一种方法是使用质谱法开发分子表型工具，以提供详细的分子代谢和蛋白质组学概况。由此产生的丰富数据集还可以支持诊断性生物标志物的开发，并提供对疾病机制的见解，从而支持更有效的治疗和管理策略。利用两种截然不同的人群，他们的免疫反应因食物过敏反应而失调，而个人在COVID-19感染期间经历了严重的免疫破坏。与工业合作伙伴Waters公司合作，两组的血浆蛋白质代谢组将使用最先进的离子迁移质谱法进行全面表征，该质谱法提供数据，允许分子的3D结构表征。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。由此产生的高维数据集也将用于识别临床表型的蛋白质组学和代谢组学标记，使用机器学习方法和贝叶斯推理来支持快速临床决策和识别疾病途径，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.