When are pathogen genome sequences informative of transmission events?
When are pathogen genome sequences informative of transmission events?
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DOI：
10.1371/journal.ppat.1006885
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发表时间：
2018-03
期刊：
影响因子：
6.7
通讯作者：
Jombart T
中科院分区：
文献类型：
--
作者：
Campbell F;Strang C;Ferguson N;Cori A;Jombart T
近年来，已经开发出多种方法，用于根据密集采样的全基因组序列数据重建传染病爆发中的传播树。然而，此类方法的一个基本且尚未得到很好解决的局限性是需要在流行病学时间尺度上出现遗传多样性。具体来说，如果采样的病原体基因组之间积累了突变，则只能通过遗传数据来确定受感染个体在传播树中的位置。为了量化和比较不同病原体爆发中遗传数据预期的有用遗传多样性，我们在此引入“传播分歧”的概念，定义为从传播对中采样的全基因组序列分离的突变数量。使用文献综述获得的参数值，我们使用文献中描述的两个模型来模拟序列进化的爆发场景，以描述十种主要引起爆发的病原体的传播分歧。我们发现，虽然所考虑的病原体之间的平均值差异很大，但它们的传播差异通常非常低，许多疫情的特点是存在大量遗传相同的传播对。我们使用两种疫情重建工具（R 包爆发器和 phybreak）描述了传播分歧对我们重建疫情能力的影响，并证明，与之前的观察结果一致，快速进化的病原体（如 RNA 病毒）的基因序列数据可以提供有关个体传播事件的有价值的信息。相反，平均传播差异较低的病原体（包括肺炎链球菌、宋内志贺氏菌和艰难梭菌）的序列数据几乎无法提供有关个体传播事件的信息。我们的结果强调了某些疫情爆发情况下基因序列数据的信息局限性，并证明需要扩展疫情重建工具包以整合其他类型的流行病学数据。基因序列数据的可用性不断增加，引发了人们对使用病原体全基因组序列来重建传染病爆发中个体传播事件的历史的兴趣。然而，这种方法依赖于病原体基因组足够快的突变来区分受感染的个体，这一假设仍有待研究。为了确定遗传数据有望为传播事件提供信息的病原体爆发，我们在此引入“传播分歧”的概念，定义为从传播对中采样的病原体基因组序列分开的突变数量。我们通过模拟描述了导致疾病爆发的十种主要病原体的传播差异，并发现疾病之间存在显着差异，病毒爆发通常比细菌爆发表现出更高的传播差异。我们使用 R-packages Outbreaker 和 phybreak 重建了这些疫情，发现基因序列数据虽然对于快速进化的病原体有用，但几乎没有提供有关低传播分歧的疫情的信息，例如肺炎链球菌和宋内氏志贺氏菌。我们的结果表明，需要将其他来源的疫情数据（例如接触者追踪数据和空间位置数据）纳入疫情重建工具中。
Recent years have seen the development of numerous methodologies for reconstructing transmission trees in infectious disease outbreaks from densely sampled whole genome sequence data. However, a fundamental and as of yet poorly addressed limitation of such approaches is the requirement for genetic diversity to arise on epidemiological timescales. Specifically, the position of infected individuals in a transmission tree can only be resolved by genetic data if mutations have accumulated between the sampled pathogen genomes. To quantify and compare the useful genetic diversity expected from genetic data in different pathogen outbreaks, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating whole genome sequences sampled from transmission pairs. Using parameter values obtained by literature review, we simulate outbreak scenarios alongside sequence evolution using two models described in the literature to describe transmission divergence of ten major outbreak-causing pathogens. We find that while mean values vary significantly between the pathogens considered, their transmission divergence is generally very low, with many outbreaks characterised by large numbers of genetically identical transmission pairs. We describe the impact of transmission divergence on our ability to reconstruct outbreaks using two outbreak reconstruction tools, the R packages outbreaker and phybreak, and demonstrate that, in agreement with previous observations, genetic sequence data of rapidly evolving pathogens such as RNA viruses can provide valuable information on individual transmission events. Conversely, sequence data of pathogens with lower mean transmission divergence, including Streptococcus pneumoniae, Shigella sonnei and Clostridium difficile, provide little to no information about individual transmission events. Our results highlight the informational limitations of genetic sequence data in certain outbreak scenarios, and demonstrate the need to expand the toolkit of outbreak reconstruction tools to integrate other types of epidemiological data. The increasing availability of genetic sequence data has sparked an interest in using pathogen whole genome sequences to reconstruct the history of individual transmission events in an infectious disease outbreak. However, such methodologies rely on pathogen genomes mutating rapidly enough to discriminate between infected individuals, an assumption that remains to be investigated. To determine pathogen outbreaks for which genetic data is expected to be informative of transmission events, we introduce here the concept of ‘transmission divergence’, defined as the number of mutations separating pathogen genome sequences sampled from transmission pairs. We characterise transmission divergence of ten major outbreak causing pathogens using simulations and find significant variation between diseases, with viral outbreaks generally exhibiting higher transmission divergence than bacterial ones. We reconstruct these outbreaks using the R-packages outbreaker and phybreak and find that genetic sequence data, though useful for rapidly evolving pathogens, provides little to no information about outbreaks with low transmission divergence, such as Streptococcus pneumoniae and Shigella sonnei. Our results demonstrate the need to incorporate other sources of outbreak data, such as contact tracing data and spatial location data, into outbreak reconstruction tools.
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影响因子：
56.3
作者：
Cauchemez, Simon;Fraser, Christophe;Van Kerkhove, Maria D.;Donnelly, Christi A.;Riley, Steven;Rambaut, Andrew;Enouf, Vincent;van der Werf, Sylvie;Ferguson, Neil M.
通讯作者：
Ferguson, Neil M.
DOI：
10.1056/nejmoa0905498
发表时间：
2009-12-31
期刊：
The New England journal of medicine
影响因子：
--
作者：
Cauchemez S;Donnelly CA;Reed C;Ghani AC;Fraser C;Kent CK;Finelli L;Ferguson NM
通讯作者：
Ferguson NM
DOI：
10.1056/nejmoa1411100
发表时间：
2014-10-16
期刊：
The New England journal of medicine
影响因子：
--
作者：
WHO Ebola Response Team;Aylward B;Barboza P;Bawo L;Bertherat E;Bilivogui P;Blake I;Brennan R;Briand S;Chakauya JM;Chitala K;Conteh RM;Cori A;Croisier A;Dangou JM;Diallo B;Donnelly CA;Dye C;Eckmanns T;Ferguson NM;Formenty P;Fuhrer C;Fukuda K;Garske T;Gasasira A;Gbanyan S;Graaff P;Heleze E;Jambai A;Jombart T;Kasolo F;Kadiobo AM;Keita S;Kertesz D;Koné M;Lane C;Markoff J;Massaquoi M;Mills H;Mulba JM;Musa E;Myhre J;Nasidi A;Nilles E;Nouvellet P;Nshimirimana D;Nuttall I;Nyenswah T;Olu O;Pendergast S;Perea W;Polonsky J;Riley S;Ronveaux O;Sakoba K;Santhana Gopala Krishnan R;Senga M;Shuaib F;Van Kerkhove MD;Vaz R;Wijekoon Kannangarage N;Yoti Z
通讯作者：
Yoti Z
影响因子：
14.9
作者：
Clark K;Karsch-Mizrachi I;Lipman DJ;Ostell J;Sayers EW
通讯作者：
Sayers EW
影响因子：
4.2
作者：
Camilli R;Bonnal RJ;Del Grosso M;Iacono M;Corti G;Rizzi E;Marchetti M;Mulas L;Iannelli F;Superti F;Oggioni MR;De Bellis G;Pantosti A
通讯作者：
Pantosti A