Xiong Jinbo, Zhu Jinyong, Dai Wenfang, Dong Chunming, Qiu Qiongfen, Li Chenghua
School of Marine Sciences, Ningbo University, Ningbo, 315211, China.
Collaborative Innovation Center for Zhejiang Marine High-Efficiency and Healthy Aquaculture, Ningbo, 315211, China.
Environ Microbiol. 2017 Apr;19(4):1490-1501. doi: 10.1111/1462-2920.13701. Epub 2017 Mar 23.
Increasing evidence has emerged a tight link among the gut microbiota, host age and health status. This osculating interplay impedes the definition of gut microbiome features associated with host health from that in developmental stages. Consequently, gut microbiota-based prediction of health status is promising yet not well established. Here we firstly tracked shrimp gut microbiota (N = 118) over an entire cycle of culture; shrimp either stayed healthy or progressively transitioned into severe disease. The results showed that the gut microbiota were significantly distinct over shrimp developmental stages and disease progression. Null model and phylogenetic-based mean nearest taxon distance (MNTD) analyses indicated that deterministic processes that governed gut community became less important as the shrimp aged and disease progressed. The predicted gut microbiota age (using the profiles of age-discriminatory bacterial species as independent variables) fitted well (r = 0.996; P < 0.001) with the age of healthy subjects, while this defined trend was disrupted by disease. Microbiota-for-age Z-scores (MAZ, here defined as immaturity) were relative stable among healthy shrimp, but sharply decreased when disease emerged. By distinguishing between age- and disease- discriminatory taxa, we developed a model, bacterial indicators of shrimp health status, to diagnose disease from healthy subjects with 91.5% accuracy. Notably, the relative abundances of the bacterial indicators were indicative for shrimp disease severity. These findings, in aggregate, add our understanding on the gut community assembly patterns over shrimp developmental stages and disease progression. In addition, shrimp disease initiation and severity can be accurately diagnosed using gut microbiota immaturity and bacterial indicators.
越来越多的证据表明，肠道微生物群、宿主年龄和健康状况之间存在紧密联系。这种密切的相互作用阻碍了将与宿主健康相关的肠道微生物组特征与发育阶段的特征区分开来。因此，基于肠道微生物群对健康状况进行预测虽有前景，但尚未完全确立。在此，我们首先在整个养殖周期内追踪了虾的肠道微生物群（N = 118）；虾要么保持健康，要么逐渐发展为严重疾病。结果表明，在虾的发育阶段和疾病进展过程中，肠道微生物群存在显著差异。零模型和基于系统发育的平均最近分类单元距离（MNTD）分析表明，随着虾的年龄增长和疾病进展，控制肠道群落的确定性过程变得不那么重要。预测的肠道微生物群年龄（使用年龄歧视性细菌物种的谱作为自变量）与健康虾的年龄拟合良好（r = 0.996；P < 0.001），而这种确定的趋势在疾病出现时被打乱。健康虾之间的微生物群与年龄的Z分数（MAZ，此处定义为不成熟度）相对稳定，但疾病出现时会急剧下降。通过区分年龄和疾病歧视性分类群，我们开发了一个模型，即虾健康状况的细菌指标，用于从健康虾中诊断疾病，准确率为91.5%。值得注意的是，细菌指标的相对丰度可指示虾病的严重程度。总体而言，这些发现增加了我们对虾发育阶段和疾病进展过程中肠道群落组装模式的理解。此外，利用肠道微生物群的不成熟度和细菌指标可以准确诊断虾病的发生和严重程度。
