Center for Computational Biology, Beijing Forestry University, Beijing 100083, China.
Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA.
Cells. 2021 Dec 28;11(1):80. doi: 10.3390/cells11010080.
Coronavirus disease (COVID-19) spreads mainly through close contact of infected persons, but the molecular mechanisms underlying its pathogenesis and transmission remain unknown. Here, we propose a statistical physics model to coalesce all molecular entities into a cohesive network in which the roadmap of how each entity mediates the disease can be characterized. We argue that the process of how a transmitter transforms the virus into a recipient constitutes a triad unit that propagates COVID-19 along reticulate paths. Intrinsically, person-to-person transmissibility may be mediated by how genes interact transversely across transmitter, recipient, and viral genomes. We integrate quantitative genetic theory into hypergraph theory to code the main effects of the three genomes as nodes, pairwise cross-genome epistasis as edges, and high-order cross-genome epistasis as hyperedges in a series of mobile hypergraphs. Charting a genome-wide atlas of horizontally epistatic hypergraphs can facilitate the systematic characterization of the community genetic mechanisms underlying COVID-19 spread. This atlas can typically help design effective containment and mitigation strategies and screen and triage those more susceptible persons and those asymptomatic carriers who are incubation virus transmitters.
冠状病毒病(COVID-19)主要通过感染者的密切接触传播,但其发病机制和传播的分子机制仍不清楚。在这里,我们提出了一个统计物理模型,将所有分子实体合并为一个有凝聚力的网络,在这个网络中,可以描述每个实体如何介导疾病的路线图。我们认为,从病毒传播到受感染者的过程构成了一个三联体单元,沿着网状路径传播 COVID-19。从本质上讲,人与人之间的传染性可能是由基因如何在 transmitter、recipient 和 viral 基因组之间横向相互作用介导的。我们将定量遗传理论整合到超图理论中,将三个基因组的主要效应作为节点,将双基因组上位性作为边,将高次跨基因组上位性作为一系列移动超图中的超边。绘制全基因组水平的水平上位性超图图谱可以促进对 COVID-19 传播的社区遗传机制的系统表征。该图谱通常有助于设计有效的遏制和缓解策略,并筛选和分诊那些更容易感染的人以及那些无症状的潜伏病毒携带者。