Institute of Soil Science and Land Evaluation, Biogeophysics Section, University of Hohenheim, Stuttgart, Germany.
Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada.
Environ Sci Technol. 2020 Nov 3;54(21):13638-13650. doi: 10.1021/acs.est.0c03315. Epub 2020 Oct 16.
Pesticides are widely used in agriculture despite their negative impact on ecosystems and human health. Biogeochemical modeling facilitates the mechanistic understanding of microbial controls on pesticide turnover in soils. We propose to inform models of coupled microbial dynamics and pesticide turnover with measurements of the abundance and expression of functional genes. To assess the advantages of informing models with genetic data, we developed a novel "gene-centric" model and compared model variants of differing structural complexity against a standard biomass-based model. The models were calibrated and validated using data from two batch experiments in which the degradation of the pesticides dichlorophenoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyacetic acid (MCPA) were observed in soil. When calibrating against data on pesticide mineralization, the gene-centric and biomass-based models performed equally well. However, accounting for pesticide-triggered gene regulation allows improved performance in capturing microbial dynamics and in predicting pesticide mineralization. This novel modeling approach also reveals a hysteretic relationship between pesticide degradation rates and gene expression, implying that the biodegradation performance in soils cannot be directly assessed by measuring the expression of functional genes. Our gene-centric model provides an effective approach for exploiting molecular biology data to simulate pesticide degradation in soils.
尽管农药对生态系统和人类健康有负面影响，但仍广泛应用于农业。生物地球化学模型有助于深入了解微生物对土壤中农药转化的控制机制。我们建议利用功能基因的丰度和表达测量来为耦合微生物动力学和农药转化模型提供信息。为了评估用遗传数据为模型提供信息的优势，我们开发了一种新颖的“基因中心”模型，并将不同结构复杂性的模型变体与基于生物量的标准模型进行了比较。使用在土壤中观察到农药二氯苯氧基乙酸（2,4-D）和 2-甲基-4-氯苯氧基乙酸（MCPA）降解的两个批量实验的数据对模型进行了校准和验证。当根据农药矿化的数据进行校准时，基因中心和基于生物量的模型表现相当。然而，考虑到农药引发的基因调控，可以更好地捕捉微生物动态并预测农药矿化。这种新的建模方法还揭示了农药降解速率与基因表达之间的滞后关系，这意味着不能通过测量功能基因的表达来直接评估土壤中生物降解的性能。我们的基因中心模型为利用分子生物学数据来模拟土壤中农药降解提供了一种有效的方法。
