Dhakar Kusum, Zarecki Raphy, van Bommel Daniella, Knossow Nadav, Medina Shlomit, Öztürk Basak, Aly Radi, Eizenberg Hanan, Ronen Zeev, Freilich Shiri
Newe Ya'ar Research Center, Agricultural Research Organization, Ramat Yishai, Israel.
Department of Environmental Hydrology & Microbiology, Zuckerberg Institute for Water Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Beersheba, Israel.
Front Bioeng Biotechnol. 2021 Apr 15;9:602464. doi: 10.3389/fbioe.2021.602464. eCollection 2021.
Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.
苯基脲类除草剂被广泛应用于农业和非农业领域的杂草控制。利谷隆是该类除草剂中的关键品种之一,应用广泛。与其他苯基脲类除草剂一样,由于其在环境中的持久性,已知具有毒性作用。利谷隆从环境中的自然去除主要通过微生物生物降解进行。据报道,一些微生物能将利谷隆完全矿化,并将其用作碳源和氮源。菌株SRS 16是已知的高效降解菌之一,其基因组最近已测序。基因组数据为利用基因组规模模型改善生物降解提供了契机。我们研究的目的是按照自动和手动方案构建一个基因组规模的代谢模型,并通过迭代模拟应用该模型来提高其代谢潜力。应用通量平衡分析(FBA),模拟了在考虑所选补充物(潜在碳源和氮源)影响的情况下,SRS 16在不同培养基中的生长和降解性能。结果是对合适的培养基改良的预测,可使SRS 16更快地降解利谷隆。选择了七种代谢物通过实验室实验验证预测结果,证实了特定氨基酸(谷氨酰胺和天冬酰胺)对利谷隆降解和SRS 16生长的促进降解作用。总体而言,模拟结果显示在存在特定补充物的情况下能有效预测SRS 16的降解潜力。所生成的信息有助于理解利谷隆降解的生物化学过程,并且无需任何基因操作即可进一步用于开发新的清理解决方案。