Application of multiscale algebra and topology to understanding heterogeneity in the immune response to SARS CoV 2 infection

应用多尺度代数和拓扑来了解 SARS CoV 2 感染免疫反应的异质性

• 批准号: EP/W01484X/1
• 负责人: Julian Knight
• 金额: $ 23.25万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW01484X%2F1
• 关键词: Application; multiscale; algebra; topology; understanding

The basis of variability between patients in their immune response and outcome from acute SARS-Cov-2 infection remains poorly defined, limiting opportunities for targetted intervention. The generation of multi-modal molecular and immunological data sets profiling the immune response across individuals and over time provides opportunities to address this but maximising the informativeness of such datasets remains a major roadblock. Here, we propose to address this through application of state-of-the-art integrative mathematical and computational techniques to analyse data together and extract novel insights. We will use algebraic systems biology approaches to combine algebraic geometry, data tensors, topological data analysis and network theory to encode multidimensional and multi-indexed data in order to identify signatures and cellular drivers of heterogeneity in the host immune response leading to different disease severity. We will apply this to data recently generated by the Oxford COVID-19 Multi-Omic Blood ATlas (COMBAT) consortium which includes high resolution clinical phenotyping, single cell profiling of the cellular blood compartment for composition, repertoire, transcriptomics and epigenomics, the plasma secretome, serology, viral sequencing, metagenomics and host genotyping. Our application is timely and urgent given availability of data and opportunity for impact. The work will promote collaboration between medical and mathematical sciences, promoting cross-disciplinarity. The analysis will provide novel insights into pathophysiology, identify key networks and nodal points for targetted intervention that will enable development of immunmodulatory therapy, and define biomarkers informative for the individual immune response that can be taken forward for validation and enable development of a precision medicine approach to COVID-19.

急性SARS-Cov-2感染患者的免疫应答和结果的差异性基础仍然不明确，限制了有针对性的干预的机会。多模态分子和免疫学数据集的生成描绘了个体之间和随着时间的推移的免疫反应，这为解决这一问题提供了机会，但最大限度地提高这些数据集的信息量仍然是一个主要的障碍。在这里，我们建议通过应用最先进的综合数学和计算技术来分析数据并提取新的见解来解决这个问题。我们将使用代数系统生物学方法，结合联合收割机代数几何，数据张量，拓扑数据分析和网络理论来编码多维和多索引的数据，以确定签名和宿主免疫反应的异质性导致不同的疾病严重程度的细胞驱动程序。我们将把它应用于牛津COVID-19多器官血液ATlas（COMBAT）联盟最近生成的数据，其中包括高分辨率临床表型分析，细胞血液成分的单细胞分析，库，转录组学和表观基因组学，血浆分泌组学，血清学，病毒测序，宏基因组学和宿主基因分型。鉴于数据的可用性和产生影响的机会，我们的申请是及时和紧迫的。这项工作将促进医学和数学科学之间的合作，促进跨学科。该分析将为病理生理学提供新的见解，确定靶向干预的关键网络和节点，从而能够开发免疫调节治疗，并定义可用于验证的个体免疫应答的生物标志物，并能够开发针对COVID-19的精准医学方法。