Support vector regression model of wastewater bioreactor performance using microbial community diversity indices: Effect of stress and bioaugmentation
Support vector regression model of wastewater bioreactor performance using microbial community diversity indices: Effect of stress and bioaugmentation
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DOI：
10.1016/j.watres.2014.01.015
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发表时间：
2014-04-15
期刊：
影响因子：
12.8
通讯作者：
Wuertz, Stefan
中科院分区：
文献类型：
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作者：
Seshan, Hari;Goyal, Manish K.;Wuertz, Stefan
微生物群落结构和功能之间的关系已在自然和工程环境中进行了详细研究，但很少有人利用微生物群落信息来预测功能。我们处理了微生物群落和操作数据与实验室规模的生物反应器系统的控制实验，以预测反应器的工艺性能。在两个实验中操作处理合成废水的四个膜操作的序批式反应器，以测试（i）有毒化合物3-氯苯胺（3-CA）和（ii）针对3-CA降解的生物强化对反应器中的污泥微生物群落的影响。在第一个实验中，两个反应器用3-CA处理，两个反应器作为对照在没有3-CA输入的情况下操作。在第二个实验中，所有四个反应器另外用恶臭假单胞菌菌株进行生物强化，所述恶臭假单胞菌菌株携带具有3-CA降解途径的一部分的质粒。分子数据产生的终端限制性片段长度多态性（T-RFLP）分析，针对16 S rRNA和amoA基因的污泥社区。从这些T-RF产生的电泳图被用来计算多样性指数-社区丰富度，动态和均匀度-为域细菌以及氨氧化细菌在每个反应器的时间。这些多样性指数，然后用于训练和测试的支持向量回归（SVR）模型，预测反应器的性能的基础上输入的微生物群落指数和操作数据。考虑到随时间变化的多样性指数和重复反应器之间的离散值，发现尽管用含有参与3-CA降解的基因子集的细菌菌株进行生物强化不会导致3-CA降解，通过一般细菌群落和氨氧化剂群落的所有三个多样性指数测量，它显著影响了群落（α = 0.5）。生物强化的影响也被定性地看到在每个反应器中随着时间的推移，随着整体的社区丰富度下降的情况下，生物强化反应器进行3-CA和社区均匀度保持较低和更稳定的生物强化反应器，而不是unbioaugmented反应器的社区丰富度和均匀度的变化。使用多样性指数，3-CA输入，生物强化和时间作为输入变量，SVR模型成功地预测了反应器的性能方面的广泛的污染物，如COD，氨和硝酸盐以及特定的污染物，如3-CA的去除。这项工作是第一个证明（i）生物强化，即使不成功，也可以产生群落结构的变化，（ii）微生物群落信息可以用于可靠地预测工艺性能。然而，T-RFLP可能无法最准确地代表微生物群落本身，并且可以使用更复杂的分子方法开发更强大的预测工具。(C)2014爱思唯尔有限公司版权所有。
The relationship between microbial community structure and function has been examined in detail in natural and engineered environments, but little work has been done on using microbial community information to predict function. We processed microbial community and operational data from controlled experiments with bench-scale bioreactor systems to predict reactor process performance. Four membrane-operated sequencing batch reactors treating synthetic wastewater were operated in two experiments to test the effects of (i) the toxic compound 3-chloroaniline (3-CA) and (ii) bioaugmentation targeting 3-CA degradation, on the sludge microbial community in the reactors. In the first experiment, two reactors were treated with 3-CA and two reactors were operated as controls without 3-CA input. In the second experiment, all four reactors were additionally bioaugmented with a Pseudomonas putida strain carrying a plasmid with a portion of the pathway for 3-CA degradation. Molecular data were generated from terminal restriction fragment length polymorphism (T-RFLP) analysis targeting the 16S rRNA and amoA genes from the sludge community. The electropherograms resulting from these T-RFs were used to calculate diversity indices - community richness, dynamics and evenness - for the domain Bacteria as well as for ammonia-oxidizing bacteria in each reactor overtime. These diversity indices were then used to train and test a support vector regression (SVR) model to predict reactor performance based on input microbial community indices and operational data. Considering the diversity indices over time and across replicate reactors as discrete values, it was found that, although bioaugmentation with a bacterial strain harboring a subset of genes involved in the degradation of 3-CA did not bring about 3-CA degradation, it significantly affected the community as measured through all three diversity indices in both the general bacterial community and the ammonia-oxidizer community (alpha = 0.5). The impact of bioaugmentation was also seen qualitatively in the variation of community richness and evenness over time in each reactor, with overall community richness falling in the case of bioaugmented reactors subjected to 3-CA and community evenness remaining lower and more stable in the bioaugmented reactors as opposed to the unbioaugmented reactors. Using diversity indices, 3-CA input, bioaugmentation and time as input variables, the SVR model successfully predicted reactor performance in terms of the removal of broad-range contaminants like COD, ammonia and nitrate as well as specific contaminants like 3-CA. This work was the first to demonstrate that (i) bioaugmentation, even when unsuccessful, can produce a change in community structure and (ii) microbial community information can be used to reliably predict process performance. However, T-RFLP may not result in the most accurate representation of the microbial community itself, and a much more powerful prediction tool can potentially be developed using more sophisticated molecular methods. (C) 2014 Elsevier Ltd. All rights reserved.