Borton Mikayla A, McGivern Bridget B, Willi Kathryn R, Woodcroft Ben J, Mosier Annika C, Singleton Derick M, Bambakidis Ted, Pelly Aaron, Liu Filipe, Edirisinghe Janaka N, Faria José P, Leleiwi Ikaia, Daly Rebecca A, Goldman Amy E, Wilkins Michael J, Hall Ed K, Pennacchio Christa, Roux Simon, Eloe-Fadrosh Emiley A, Good Stephen P, Sullivan Matthew B, Henry Christopher S, Wood-Charlson Elisha M, Ross Matthew R V, Miller Christopher S, Crump Byron C, Stegen James C, Wrighton Kelly C
bioRxiv. 2023 Jul 26:2023.07.22.550117. doi: 10.1101/2023.07.22.550117.
Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial processes governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling, sequencing, and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). This resource profiled the identity, distribution, function, and expression of thousands of microbial genomes across rivers covering 90% of United States watersheds. Specifically, GROWdb encompasses 1,469 microbial species from 27 phyla, including novel lineages from 10 families and 128 genera, and defines the core river microbiome for the first time at genome level. GROWdb analyses coupled to extensive geospatial information revealed local and regional drivers of microbial community structuring, while also presenting a myriad of foundational hypotheses about ecosystem function. Building upon the previously conceived River Continuum Concept , we layer on microbial functional trait expression, which suggests the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures so that it can be widely accessed across disciplines for watershed predictive modeling and microbiome-based management practices.
在人为影响不断增加的情况下预测元素循环并维持水质,需要了解河流微生物群落的空间驱动因素。然而,由于缺乏基因组解析的功能见解以及跨多条河流的采样,统一的河流生物地球化学微生物过程受到了阻碍。在这里,我们通过社区科学努力,加速河流微生物群落的采样、测序和基因组解析分析,以创建基因组解析开放流域数据库(GROWdb)。该资源描绘了覆盖美国90%流域的多条河流中数千个微生物基因组的身份、分布、功能和表达情况。具体而言,GROWdb包含来自27个门的1469种微生物,包括来自10个科和128个属的新谱系,并首次在基因组水平上定义了核心河流微生物群落。结合广泛地理空间信息的GROWdb分析揭示了微生物群落结构的局部和区域驱动因素,同时还提出了关于生态系统功能的众多基础假设。在先前提出的河流连续体概念的基础上,我们叠加了微生物功能性状表达,这表明河流微生物群落的结构和功能是可预测的。我们通过各种协作式网络基础设施提供GROWdb,以便跨学科广泛访问,用于流域预测建模和基于微生物群落的管理实践。