Duke University, Department of Civil and Environmental Engineering, 121 Hudson Hall, Durham, NC 27708-0287, United States.
Duke University, Department of Civil and Environmental Engineering, 121 Hudson Hall, Durham, NC 27708-0287, United States.
Environ Int. 2019 Mar;124:312-319. doi: 10.1016/j.envint.2018.12.038. Epub 2019 Jan 17.
While ballast water has long been linked to the global transport of invasive species, little is known about its microbiome. Herein, we used 16S rRNA gene sequencing and metabarcoding to perform the most comprehensive microbiological survey of ballast water arriving to hub ports to date. In total, we characterized 41 ballast, 20 harbor, and 6 open ocean water samples from four world ports (Shanghai, China; Singapore; Durban, South Africa; Los Angeles, California). In addition, we cultured Enterococcus and E. coli to evaluate adherence to International Maritime Organization standards for ballast discharge. Five of the 41 vessels - all of which were loaded in China - did not comply with standards for at least one indicator organism. Dominant bacterial taxa of ballast water at the class level were Alphaproteobacteria, Gammaproteobacteria, and Bacteroidia. Ballast water samples were composed of significantly lower proportions of Oxyphotobacteria than either ocean or harbor samples. Linear discriminant analysis (LDA) effect size (LEfSe) and machine learning were used to identify and test potential biomarkers for classifying sample types (ocean, harbor, ballast). Eight candidate biomarkers were used to achieve 81% (k nearest neighbors) to 88% (random forest) classification accuracy. Further research of these biomarkers could aid the development of techniques to rapidly assess ballast water origin.
虽然压载水长期以来一直与入侵物种的全球运输有关，但对其微生物组知之甚少。在此，我们使用 16S rRNA 基因测序和代谢组学对迄今为止到达枢纽港口的压载水进行了最全面的微生物调查。总共对来自四个世界港口（中国上海；新加坡；南非德班；加利福尼亚洛杉矶）的 41 个压载水、20 个港口和 6 个公海水样本进行了特征描述。此外，我们还培养了肠球菌和大肠杆菌，以评估其对国际海事组织压载水排放标准的遵守情况。在这 41 艘船只中，有 5 艘（均在中国装货）至少有一项指标不符合标准。压载水的优势细菌类群在纲水平上为α变形菌纲、γ变形菌纲和拟杆菌纲。与海洋或港口样本相比，压载水样本中的 Oxyphotobacteria 比例明显较低。线性判别分析（LDA）效应大小（LEfSe）和机器学习用于识别和测试用于对样本类型（海洋、港口、压载水）进行分类的潜在生物标志物。使用 8 个候选生物标志物实现了 81%（k 最近邻）到 88%（随机森林）的分类准确性。对这些生物标志物的进一步研究可以帮助开发快速评估压载水来源的技术。
