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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Bacterial community structures are unique and resilient in full-scale bioenergy systems
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Bacterial community structures are unique and resilient in full-scale bioenergy systems

机译：细菌群落结构在完整的生物能源系统中具有独特性和弹性

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Anaerobic digestion is the most successful bioenergy technology worldwide with, at its core, undefined microbial communities that have poorly understood dynamics. Here, we investigated the relationships of bacterial community structure (>400,00016S rRNA gene sequences for 112 samples) with function (i.e., bioreactor performance) and environment (i.e., operating conditions) in a yearlong monthly time series of nine full-scale bioreactor facilities treating brewery wastewater (>20,000 measurements). Each of the nine facilities had a unique community structure with an unprecedented level of stability. Using machine learning, we identified a small subset of operational taxonomic units (OTUs; 145 out of 4,962), which predicted the location of the facility of origin for almost every sample (96.4% accuracy). Of these 145 OTUs, syntro-phic bacteria were systematically overrepresented, demonstrating that syntrophs rebounded following disturbances. This indicates that resilience, rather than dynamic competition, played an important role in maintaining the necessary syntrophic populations. In addition, we explained the observed phylogenetic differences between all samples on the basis of a subset of environmental gradients (using constrained ordination) and found stronger relationships between community structure and its function rather than its environment. These relationships were strongest for two performance variables-methanogenic activity and substrate removal efficiency-both of which were also affected by microbial ecology because these variables were correlated with community evenness (at any given time) and variability in phylogenetic structure (over time), respectively. Thus, we quantified relationships between community structure and function, which opens the door to engineer communities with superior functions.
机译：厌氧消化是世界上最成功的生物能源技术，其核心是不确定的微生物群落，对微生物的动力学了解甚少。在这里，我们调查了九个全尺寸生物反应器的为期一年的每月时间序列中细菌群落结构（112个样品的> 400,00016S rRNA基因序列）与功能（即生物反应器性能）和环境（即操作条件）之间的关系。处理啤酒废水的设施（> 20,000个测量值）。九个设施中的每一个都具有独特的社区结构，具有前所未有的稳定性。使用机器学习，我们确定了操作分类单元的一小部分（OTU； 4,962个中的145个），该单元预测了几乎每个样本的起源设施的位置（准确度为96.4％）。在这145个OTU中，同养细菌被系统地代表了过多，表明同养细菌在干扰后反弹。这表明，在保持必要的同养群体中，韧性而非动态竞争起着重要作用。此外，我们根据环境梯度的一个子集（使用约束排序）解释了所有样本之间观察到的系统发育差异，并发现了群落结构与其功能而不是环境之间更紧密的关系。这些关系对于两个性能变量（产甲烷活性和底物去除效率）最强，这两个变量也都受到微生物生态学的影响，因为这些变量分别与群落均匀性（在任何给定时间）和系统发育结构的变异性（随时间）相关。 。因此，我们量化了社区结构与功能之间的关系，这为设计功能卓越的社区打开了大门。

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