Division of Computer, Electrical and Mathematical Sciences and Engineering, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
College of Computer Science and Engineering (CCSE), Taibah University, Medina, Saudi Arabia.
Hum Genomics. 2023 Mar 2;17(1):17. doi: 10.1186/s40246-023-00454-y.
Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks.
We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction.
This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.
全基因组关联研究已经确定了许多人类宿主遗传风险变异，这些变异在宿主对 SARS-CoV-2 的免疫反应中起着重要作用。尽管这些遗传风险变异显著增加了 COVID-19 的严重程度，但它们对身体系统的影响还不清楚。因此，我们旨在解释与严重 COVID-19 相关的遗传风险因素和免疫反应的生物学机制和途径。我们对先前确定的风险变异进行了深入分析，并通过疾病映射和构建网络之间分子功能的相似性来推断它们分子网络之间的隐藏相互作用。
我们设计了一个四阶段的计算工作流程，用于对风险变异进行系统的遗传分析。我们将风险因素的分子特征与相关疾病相结合，然后构建蛋白质-蛋白质相互作用网络。我们从 60 个风险基因和 56 个蛋白质中筛选出的 109 个过滤风险变体中确定了 24 个具有 939 个相互作用的蛋白质-蛋白质相互作用网络。大多数分子功能、相互作用和途径都涉及免疫反应；一些相互作用和途径与代谢和心血管系统有关，这可能导致多器官并发症和功能障碍。
本研究强调了分析分子相互作用和途径以了解宿主对 SARS-CoV-2 免疫反应的异质性易感性的重要性。我们提出了一种新的感染致病性分析方法，将遗传风险信息作为预测感染期间和之后未来并发症的重要因素纳入其中。这种方法可能有助于更精确的临床决策和准确的治疗计划，以减少 COVID-19 的并发症。
