Dynamics of microbial populations mediating biogeochemical cycling in a freshwater lake.
Dynamics of microbial populations mediating biogeochemical cycling in a freshwater lake.
复制标题
DOI：
10.1186/s40168-018-0556-7
复制
发表时间：
2018-09-18
期刊：
影响因子：
15.5
通讯作者：
Preheim SP
中科院分区：
文献类型：
--
作者：
Arora-Williams K;Olesen SW;Scandella BP;Delwiche K;Spencer SJ;Myers EM;Abraham S;Sooklal A;Preheim SP
微生物过程与陆地和沿海水体中氧气的消耗有着错综复杂的联系，并带来了毁灭性的经济和生态后果。微生物在生物质分解过程中耗尽氧气，使许多具有重要经济意义的水生动物的栖息地退化。然后微生物转向替代电子受体，改变营养循环并产生强大的温室气体。随着土地利用和气候变化的改变，氧气消耗预计会恶化，了解化学和微生物动力学如何影响死区将有助于建模工作，以指导修复策略。需要更多的工作来了解微生物基因之间的复杂相互作用，种群，和缺氧期间的生物地球化学。在这里，我们使用16 S rRNA基因调查，鸟枪宏基因组测序，和以前开发的生物地球化学模型，以确定在一个模型湖泊生态系统中的主要生物地球化学转化涉及的基因和微生物种群。鸟枪宏基因组测序在8月的一个时间点进行，2013年，并进行了5个月时间序列的16 S rRNA基因测序（3月-8月，2013）来捕获介导建模过程的基因和微生物的时空动态。宏基因组分箱分析产生了许多宏基因组组装的基因组（MAG），其通过与培养的生物体的基因内容相似性和参与这些途径的关键基因的存在而参与建模过程。MAG表明，一些种群能够进行甲烷和硫化物氧化，并伴有硝酸盐还原。使用该模型，我们观察到，调制这些过程有一个重大的影响，对整个湖泊生态地球化学。此外，来自宏基因组和扩增子文库的16 S rRNA基因序列通过MAG与过程连接。我们将水柱中微生物种群的动态与模型预测进行了比较。许多参与初级碳氧化的微生物种群具有与模型相似的动力学，而与次级氧化过程相关的微生物种群则大幅偏离。这项工作表明，常驻微生物种群的独特能力将大大影响水柱中化学品的浓度和形态，除非其他微生物过程进行调整以弥补这些差异。它进一步突出了生物地球化学过程的生物方面的重要性，如微生物种群动态的波动。将基因和种群动态整合到生态地球化学模型中，有可能改善对改变情景下社区反应的预测，以指导补救工作。本文的在线版本（10.1186/s40168-018-0556-7）包含补充材料，可供授权用户使用。
Microbial processes are intricately linked to the depletion of oxygen in in-land and coastal water bodies, with devastating economic and ecological consequences. Microorganisms deplete oxygen during biomass decomposition, degrading the habitat of many economically important aquatic animals. Microbes then turn to alternative electron acceptors, which alter nutrient cycling and generate potent greenhouse gases. As oxygen depletion is expected to worsen with altered land use and climate change, understanding how chemical and microbial dynamics impact dead zones will aid modeling efforts to guide remediation strategies. More work is needed to understand the complex interplay between microbial genes, populations, and biogeochemistry during oxygen depletion. Here, we used 16S rRNA gene surveys, shotgun metagenomic sequencing, and a previously developed biogeochemical model to identify genes and microbial populations implicated in major biogeochemical transformations in a model lake ecosystem. Shotgun metagenomic sequencing was done for one time point in Aug., 2013, and 16S rRNA gene sequencing was done for a 5-month time series (Mar.–Aug., 2013) to capture the spatiotemporal dynamics of genes and microorganisms mediating the modeled processes. Metagenomic binning analysis resulted in many metagenome-assembled genomes (MAGs) that are implicated in the modeled processes through gene content similarity to cultured organism and the presence of key genes involved in these pathways. The MAGs suggested some populations are capable of methane and sulfide oxidation coupled to nitrate reduction. Using the model, we observe that modulating these processes has a substantial impact on overall lake biogeochemistry. Additionally, 16S rRNA gene sequences from the metagenomic and amplicon libraries were linked to processes through the MAGs. We compared the dynamics of microbial populations in the water column to the model predictions. Many microbial populations involved in primary carbon oxidation had dynamics similar to the model, while those associated with secondary oxidation processes deviated substantially. This work demonstrates that the unique capabilities of resident microbial populations will substantially impact the concentration and speciation of chemicals in the water column, unless other microbial processes adjust to compensate for these differences. It further highlights the importance of the biological aspects of biogeochemical processes, such as fluctuations in microbial population dynamics. Integrating gene and population dynamics into biogeochemical models has the potential to improve predictions of the community response under altered scenarios to guide remediation efforts. The online version of this article (10.1186/s40168-018-0556-7) contains supplementary material, which is available to authorized users.
登录
查看更多内容
影响因子：
11
作者：
Cuypers, Yannis;Vincon-Leite, Brigitte;Humbert, Jean-Francois
通讯作者：
Humbert, Jean-Francois
DOI：
10.1073/pnas.1203287109
发表时间：
2012-07-03
影响因子：
11.1
作者：
Fan, Lu;Reynolds, David;Thomas, Torsten
通讯作者：
Thomas, Torsten
影响因子：
1.7
作者：
Bankevich, Anton;Nurk, Sergey;Pevzner, Pavel A.
通讯作者：
Pevzner, Pavel A.
DOI：
10.1073/pnas.1411617111
发表时间：
2014-12-23
影响因子：
11.1
作者：
Deutzmann, Joerg S.;Stief, Peter;Schink, Bernhard
通讯作者：
Schink, Bernhard
影响因子：
64.8
作者：
Boetius, A;Ravenschlag, K;Pfannkuche, O
通讯作者：
Pfannkuche, O