Machine learning to predict the source of campylobacteriosis using whole genome data.
Machine learning to predict the source of campylobacteriosis using whole genome data.
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DOI：
10.1371/journal.pgen.1009436
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发表时间：
2021-10
期刊：
影响因子：
4.5
通讯作者：
Wilson DJ
中科院分区：
文献类型：
--
作者：
Arning N;Sheppard SK;Bayliss S;Clifton DA;Wilson DJ
弯曲杆菌病是世界上最常见的食源性疾病之一，主要由空肠弯曲杆菌引起。有效的干预措施需要确定感染源，这是一项具有挑战性的工作，因为传播可通过多种来源，如受污染的肉类、家禽和饮用水。菌株变异允许基于多位点序列分型（MLST）基因的等位基因变异进行源追踪，从而使来自受感染个体的分离物归因于特定的动物或环境宿主。然而，概率归因模型的准确性受到仅基于7个MLST基因区分分离株的能力的限制。在这里，我们扩大了输入数据频谱，包括核心基因组MLST （cgMLST）和全基因组序列（WGS），并实现了多种机器学习算法，从而实现更准确的来源归属。我们将使用标准iSource群体遗传方法的归因准确率从64%提高到MLST的71%，cgMLST的85%和使用我们命名为aiSource的分类器的聚合WGS数据的78%。为了深入了解来源模型预测之外的内容，我们使用贝叶斯推断分析了空肠梭菌菌株感染人类的相对亲和力，并确定了最常见致病谱系（ST-21克隆复合体）中克隆相关分离株在源-人传播能力方面的潜在差异。基于机器学习和群体遗传学，我们提供了一种可扩展的全球疾病监测方法，可以不断地将新样本纳入来源归因，并识别传播潜力的精细尺度变化。空肠梭菌是食源性细菌性肠胃炎最常见的原因，但不同来源的相对贡献尚不完全清楚。我们使用机器学习算法来追踪人类空肠杆菌感染的起源，该算法比较了从受感染的人、受污染的鸡、牛、羊、野生鸟类和环境中采集的细菌的DNA序列。与仅使用基因组内基因子集的现有方法相比，该方法将源归因的准确性提高了33%，并为不同感染源的相对贡献提供了证据。有时甚至非常相似的细菌也会显示出差异，这表明在开发该算法时基于全基因组分析的价值，该算法可用于了解全球流行病学和其他重要的细菌感染。
Campylobacteriosis is among the world’s most common foodborne illnesses, caused predominantly by the bacterium Campylobacter jejuni. Effective interventions require determination of the infection source which is challenging as transmission occurs via multiple sources such as contaminated meat, poultry, and drinking water. Strain variation has allowed source tracking based upon allelic variation in multi-locus sequence typing (MLST) genes allowing isolates from infected individuals to be attributed to specific animal or environmental reservoirs. However, the accuracy of probabilistic attribution models has been limited by the ability to differentiate isolates based upon just 7 MLST genes. Here, we broaden the input data spectrum to include core genome MLST (cgMLST) and whole genome sequences (WGS), and implement multiple machine learning algorithms, allowing more accurate source attribution. We increase attribution accuracy from 64% using the standard iSource population genetic approach to 71% for MLST, 85% for cgMLST and 78% for kmerized WGS data using the classifier we named aiSource. To gain insight beyond the source model prediction, we use Bayesian inference to analyse the relative affinity of C. jejuni strains to infect humans and identified potential differences, in source-human transmission ability among clonally related isolates in the most common disease causing lineage (ST-21 clonal complex). Providing generalizable computationally efficient methods, based upon machine learning and population genetics, we provide a scalable approach to global disease surveillance that can continuously incorporate novel samples for source attribution and identify fine-scale variation in transmission potential. C. jejuni are the most common cause of food-borne bacterial gastroenteritis but the relative contribution of different sources is incompletely understood. We traced the origin of human C. jejuni infections using machine learning algorithms that compare the DNA sequences of bacteria sampled from infected people, contaminated chickens, cattle, sheep, wild birds, and the environment. This approach achieved improvement in accuracy of source attribution by 33% over existing methods that use only a subset of genes within the genome and provided evidence for the relative contribution of different infection sources. Sometimes even very similar bacteria showed differences, demonstrating the value of basing analyses on the entire genome when developing this algorithm that can be used for understanding the global epidemiology and other important bacterial infections.
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开放式细菌种群基因组学：BIGSDB软件，pubmlst.org网站及其应用。
影响因子：
--
作者：
Jolley KA;Bray JE;Maiden MCJ
通讯作者：
Maiden MCJ
影响因子：
4.4
作者：
Chen, Xi;Ishwaran, Hemant
通讯作者：
Ishwaran, Hemant
通才弯曲杆菌菌株中快速的宿主切换侵蚀了追踪人类感染的信号。
DOI：
10.1038/ismej.2015.149
发表时间：
2016-03
期刊：
The ISME journal
影响因子：
--
作者：
Dearlove BL;Cody AJ;Pascoe B;Méric G;Wilson DJ;Sheppard SK
通讯作者：
Sheppard SK
影响因子：
3.3
作者：
Ansari MA;Didelot X
通讯作者：
Didelot X
影响因子：
4.6
作者：
Kirk, Karina Frahm;Meric, Guillaume;Nielsen, Henrik
通讯作者：
Nielsen, Henrik