Rein Arno, Fernqvist Margit M, Mayer Philipp, Trapp Stefan, Bittens Martin, Karlson Ulrich Gosewinkel
Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, 04318, Leipzig, Germany.
Appl Microbiol Biotechnol. 2007 Nov;77(2):469-81. doi: 10.1007/s00253-007-1175-6. Epub 2007 Sep 21.
Biological in situ methods are options for the remediation of contaminated sites. An approach to quantify biodegradation by soil bacteria was developed, combining experiment with mathematical modelling. We performed in vitro assays to investigate the potential and kinetics of the wild-type degrader, Burkholderia sp. strain LB400 (expressing bph) and the genetically modified Pseudomonas fluorescens strains F113pcb and F113L::1180 (expressing bph under different promoters) to metabolise individual congeners of polychlorinated biphenyls (PCBs). Kinetics of metabolism was analysed using the Monod model. Results revealed similar patterns of degradable PCB congeners for LB400 and F113L::1180. The degree of PCB degradation was comparable for LB400 and F113L::1180 but was much lower for F113rifpcb. In additional mesocosm experiments with PCB-contaminated soil, the F113 derivatives demonstrated a good survival ability in willow (Salix sp.) rhizosphere. Strain F113L::1180 in combination with willow plants is expected to degrade a large spectrum of PCB congeners in soil. The data from the experiments were used to calculate the time scale of the degradation process in a PCB-contaminated soil. The uncertainty of the model predictions due to the uncertainties of experimental removal velocities and bacterial cell density in soil was quantified.
生物原位修复方法是治理受污染场地的选择之一。本文开发了一种结合实验与数学建模来量化土壤细菌生物降解作用的方法。我们进行了体外试验，以研究野生型降解菌伯克霍尔德氏菌属菌株LB400（表达bph）以及转基因荧光假单胞菌菌株F113pcb和F113L::1180（在不同启动子下表达bph）代谢多氯联苯（PCBs）各同系物的潜力和动力学。使用莫诺德模型分析代谢动力学。结果显示，LB400和F113L::1180对可降解PCBs同系物具有相似的模式。LB400和F113L::1180的PCB降解程度相当，但F113rifpcb的降解程度要低得多。在另外的含PCB污染土壤的中宇宙实验中，F113衍生物在柳树（柳属）根际表现出良好的存活能力。预计F113L::1180菌株与柳树植物结合可降解土壤中多种PCB同系物。实验数据用于计算PCB污染土壤中降解过程的时间尺度。对由于实验去除速度和土壤中细菌细胞密度的不确定性导致的模型预测不确定性进行了量化。
