Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina.
Center for Genomic and Computational Biology, Duke University, Durham, North Carolina.
J Infect Dis. 2018 Jul 13;218(4):645-653. doi: 10.1093/infdis/jiy192.
Cholera is a public health problem worldwide, and the risk factors for infection are only partially understood.
We prospectively studied household contacts of patients with cholera to compare those who were infected to those who were not. We constructed predictive machine learning models of susceptibility, using baseline gut microbiota data. We identified bacterial taxa associated with susceptibility to Vibrio cholerae infection and tested these taxa for interactions with V. cholerae in vitro.
We found that machine learning models based on gut microbiota, as well as models based on known clinical and epidemiological risk factors, predicted V. cholerae infection. A predictive gut microbiota of roughly 100 bacterial taxa discriminated between contacts who developed infection and those who did not. Susceptibility to cholera was associated with depleted levels of microbes from the phylum Bacteroidetes. By contrast, a microbe associated with cholera by our modeling framework, Paracoccus aminovorans, promoted the in vitro growth of V. cholerae. Gut microbiota structure, clinical outcome, and age were also linked.
These findings support the hypothesis that abnormal gut microbial communities are a host factor related to V. cholerae susceptibility.
霍乱是一个全球性的公共卫生问题，其感染的危险因素仅部分被了解。
我们前瞻性地研究了霍乱患者的家庭接触者，以比较感染和未感染的接触者。我们使用基线肠道微生物组数据构建了易感性预测性机器学习模型。我们确定了与霍乱弧菌感染易感性相关的细菌分类群，并在体外测试了这些分类群与霍乱弧菌的相互作用。
我们发现，基于肠道微生物组的机器学习模型，以及基于已知临床和流行病学危险因素的模型，都可以预测霍乱弧菌感染。一个由大约 100 个细菌分类群组成的预测性肠道微生物组可以区分发生感染的接触者和未发生感染的接触者。对霍乱的易感性与厚壁菌门微生物的耗竭水平有关。相比之下，我们的建模框架中与霍乱相关的一种微生物，Paracoccus aminovorans，促进了霍乱弧菌的体外生长。肠道微生物组结构、临床结局和年龄也存在关联。
这些发现支持了这样一种假设，即异常的肠道微生物群落是与霍乱弧菌易感性相关的宿主因素。
