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微生物种群对慢性辐射与其他应激源联合作用的定量建模

Quantitative Modeling of Microbial Population Responses to Chronic Irradiation Combined with Other Stressors.

作者信息

Shuryak Igor, Dadachova Ekaterina

机构信息

Center for Radiological Research, Columbia University, New York, NY, United States of America.

Department of Radiology, Albert Einstein College of Medicine, Bronx, New York, United States of America.

出版信息

PLoS One. 2016 Jan 25;11(1):e0147696. doi: 10.1371/journal.pone.0147696. eCollection 2016.

Abstract

Microbial population responses to combined effects of chronic irradiation and other stressors (chemical contaminants, other sub-optimal conditions) are important for ecosystem functioning and bioremediation in radionuclide-contaminated areas. Quantitative mathematical modeling can improve our understanding of these phenomena. To identify general patterns of microbial responses to multiple stressors in radioactive environments, we analyzed three data sets on: (1) bacteria isolated from soil contaminated by nuclear waste at the Hanford site (USA); (2) fungi isolated from the Chernobyl nuclear-power plant (Ukraine) buildings after the accident; (3) yeast subjected to continuous γ-irradiation in the laboratory, where radiation dose rate and cell removal rate were independently varied. We applied generalized linear mixed-effects models to describe the first two data sets, whereas the third data set was amenable to mechanistic modeling using differential equations. Machine learning and information-theoretic approaches were used to select the best-supported formalism(s) among biologically-plausible alternatives. Our analysis suggests the following: (1) Both radionuclides and co-occurring chemical contaminants (e.g. NO2) are important for explaining microbial responses to radioactive contamination. (2) Radionuclides may produce non-monotonic dose responses: stimulation of microbial growth at low concentrations vs. inhibition at higher ones. (3) The extinction-defining critical radiation dose rate is dramatically lowered by additional stressors. (4) Reproduction suppression by radiation can be more important for determining the critical dose rate, than radiation-induced cell mortality. In conclusion, the modeling approaches used here on three diverse data sets provide insight into explaining and predicting multi-stressor effects on microbial communities: (1) the most severe effects (e.g. extinction) on microbial populations may occur when unfavorable environmental conditions (e.g. fluctuations of temperature and/or nutrient levels) coincide with radioactive contamination; (2) an organism's radioresistance and bioremediation efficiency in rich laboratory media may be insufficient to carry out radionuclide bioremediation in the field--robustness against multiple stressors is needed.

摘要

微生物种群对长期辐射与其他应激源(化学污染物、其他次优条件)综合作用的响应,对于放射性核素污染区域的生态系统功能和生物修复而言至关重要。定量数学建模有助于增进我们对这些现象的理解。为确定微生物在放射性环境中对多种应激源的响应的一般模式,我们分析了三组数据集,分别涉及:(1)从美国汉福德核废料污染土壤中分离出的细菌;(2)切尔诺贝利核电站(乌克兰)事故后从建筑物中分离出的真菌;(3)在实验室中接受连续γ辐射的酵母,其中辐射剂量率和细胞去除率可独立变化。我们应用广义线性混合效应模型来描述前两组数据集,而第三组数据集则适合使用微分方程进行机理建模。机器学习和信息论方法用于在生物学上合理的备选方案中选择最有依据的形式体系。我们的分析表明:(1)放射性核素和同时存在的化学污染物(如NO₂)对于解释微生物对放射性污染的响应均很重要。(2)放射性核素可能产生非单调剂量响应:低浓度时刺激微生物生长,而高浓度时则抑制生长。(3)额外的应激源会显著降低界定灭绝的临界辐射剂量率。(4)对于确定临界剂量率而言,辐射对繁殖的抑制作用可能比辐射诱导的细胞死亡更为重要。总之,本文对三组不同数据集所采用的建模方法,有助于深入理解和预测多应激源对微生物群落的影响:(1)当不利的环境条件(如温度和/或营养水平波动)与放射性污染同时出现时,可能对微生物种群产生最严重的影响(如灭绝);(2)生物体在丰富的实验室培养基中的抗辐射性和生物修复效率,可能不足以在实地进行放射性核素生物修复——需要具备对多种应激源的耐受性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b88/4726741/70be9d561d52/pone.0147696.g001.jpg

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