Department of Infectious Diseases, Imperial College London, London, United Kingdom.
Department of Infection, Immunity and Inflammation, University College London, London, United Kingdom.
Elife. 2023 Sep 21;12:e84384. doi: 10.7554/eLife.84384.
Accurate inference of who infected whom in an infectious disease outbreak is critical for the delivery of effective infection prevention and control. The increased resolution of pathogen whole-genome sequencing has significantly improved our ability to infer transmission events. Despite this, transmission inference often remains limited by the lack of genomic variation between the source case and infected contacts. Although within-host genetic diversity is common among a wide variety of pathogens, conventional whole-genome sequencing phylogenetic approaches exclusively use consensus sequences, which consider only the most prevalent nucleotide at each position and therefore fail to capture low-frequency variation within samples. We hypothesized that including within-sample variation in a phylogenetic model would help to identify who infected whom in instances in which this was previously impossible. Using whole-genome sequences from SARS-CoV-2 multi-institutional outbreaks as an example, we show how within-sample diversity is partially maintained among repeated serial samples from the same host, it can transmitted between those cases with known epidemiological links, and how this improves phylogenetic inference and our understanding of who infected whom. Our technique is applicable to other infectious diseases and has immediate clinical utility in infection prevention and control.
在传染病爆发中准确推断谁感染了谁对于实施有效的感染预防和控制至关重要。病原体全基因组测序分辨率的提高极大地提高了我们推断传播事件的能力。尽管如此,由于源病例和感染接触者之间缺乏基因组变异,传播推断仍然常常受到限制。尽管在各种病原体中,宿主内遗传多样性很常见,但传统的全基因组测序系统发育方法仅使用共识序列,这些序列仅考虑每个位置最常见的核苷酸,因此无法捕获样本内低频变异。我们假设在系统发育模型中包含样本内变异将有助于在以前不可能的情况下确定谁感染了谁。我们使用 SARS-CoV-2 多机构爆发的全基因组序列作为示例,展示了在同一宿主的重复序列样本中如何部分保留样本内多样性,它可以在具有已知流行病学联系的病例之间传播,以及这如何改进系统发育推断并帮助我们了解谁感染了谁。我们的技术适用于其他传染病,在感染预防和控制方面具有直接的临床应用价值。