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一种用于测量宿主内亲缘关系并区分共传播与重复感染的遗传指标。

: a genetic metric for measuring intrahost relatedness and distinguishing cotransmission from superinfection.

作者信息

Wong Wesley, Volkman Sarah, Daniels Rachel, Schaffner Stephen, Sy Mouhamad, Ndiaye Yaye Die, Badiane Aida S, Deme Awa B, Diallo Mamadou Alpha, Gomis Jules, Sy Ngayo, Ndiaye Daouda, Wirth Dyann F, Hartl Daniel L

机构信息

Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA 02115, USA.

Infectious Disease and Microbiome Program, Broad Institute, Cambridge, MA 02142, USA.

出版信息

PNAS Nexus. 2022 Sep 10;1(4):pgac187. doi: 10.1093/pnasnexus/pgac187. eCollection 2022 Sep.

Abstract

Multiple-strain (polygenomic) infections are a ubiquitous feature of parasite population genetics. Under simple assumptions of superinfection, polygenomic infections are hypothesized to be the result of multiple infectious bites. As a result, polygenomic infections have been used as evidence of repeat exposure and used to derive genetic metrics associated with high transmission intensity. However, not all polygenomic infections are the result of multiple infectious bites. Some result from the transmission of multiple, genetically related strains during a single infectious bite (cotransmission). Superinfection and cotransmission represent two distinct transmission processes, and distinguishing between the two could improve inferences regarding parasite transmission intensity. Here, we describe a new metric, , that utilizes the correlation in allelic state (heterozygosity) within polygenomic infections to estimate the likelihood that the observed complexity resulted from either superinfection or cotransmission. is flexible and can be applied to any type of genetic data. As a proof of concept, we used to quantify polygenomic relatedness and estimate cotransmission and superinfection rates from a set of 1,758 malaria infections genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode. Contrary to expectation, we found that cotransmission was responsible for a significant fraction of 43% to 53% of the polygenomic infections collected in three distinct epidemiological regions in Senegal. The prediction that polygenomic infections frequently result from cotransmission stresses the need to incorporate estimates of relatedness within polygenomic infections to ensure the accuracy of genomic epidemiology surveillance data for informing public health activities.

摘要

多菌株(多基因组)感染是寄生虫群体遗传学中普遍存在的特征。在超级感染的简单假设下,多基因组感染被假定为多次感染性叮咬的结果。因此,多基因组感染已被用作重复暴露的证据,并用于推导与高传播强度相关的遗传指标。然而,并非所有的多基因组感染都是多次感染性叮咬的结果。有些是在单次感染性叮咬期间多种遗传相关菌株传播(共传播)的结果。超级感染和共传播代表两个不同的传播过程,区分两者可以改进对寄生虫传播强度的推断。在这里,我们描述了一种新的指标,即利用多基因组感染中等位基因状态(杂合性)的相关性来估计观察到的复杂性是由超级感染还是共传播导致的可能性。该指标具有灵活性,可应用于任何类型的遗传数据。作为概念验证,我们使用该指标量化多基因组相关性,并从一组用24个单核苷酸多态性(SNP)分子条形码进行基因分型的1758例疟疾感染中估计共传播和超级感染率。与预期相反,我们发现在塞内加尔三个不同的流行地区收集的多基因组感染中,有43%至53%的很大一部分是由共传播导致的。多基因组感染频繁由共传播导致这一预测强调了需要纳入多基因组感染内相关性的估计,以确保基因组流行病学监测数据用于为公共卫生活动提供信息的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e84/9802447/010d3dbe2b8a/pgac187fig1.jpg

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