Newe Ya'ar Research Center, Agricultural Research Organizationgrid.410498.0, Ramat Yishay, Israel.
Department of Environmental Hydrology & Microbiology, Zuckerberg Institute for Water Research, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negevgrid.7489.2, Midreshet Ben-Gurion, Israel.
mSystems. 2022 Aug 30;7(4):e0016922. doi: 10.1128/msystems.00169-22. Epub 2022 Aug 1.
Extensive use of agrochemicals is emerging as a serious environmental issue coming at the cost of the pollution of soil and water resources. Bioremediation techniques such as biostimulation are promising strategies used to remove pollutants from agricultural soils by supporting the indigenous microbial degraders. Though considered cost-effective and eco-friendly, the success rate of these strategies typically varies, and consequently, they are rarely integrated into commercial agricultural practices. In the current study, we applied metabolic-based community-modeling approaches for promoting realistic solutions by simulation-based prioritization of alternative supplements as potential biostimulants, considering a collection of indigenous bacteria. Efficacy of biostimulants as enhancers of the indigenous degrader was ranked through simulation and validated in pot experiments. A two-dimensional simulation matrix predicting the effect of different biostimulants on additional potential indigenous degraders (Pseudomonas, , and ) was crossed with experimental observations. The overall ability of the models to predict the compounds that act as taxa-selective stimulants indicates that computational algorithms can guide the manipulation of the soil microbiome and provides an additional step toward the educated design of biostimulation strategies. Providing the food requirements of a growing population comes at the cost of intensive use of agrochemicals, including pesticides. Native microbial soil communities are considered key players in the degradation of such exogenous substances. Manipulating microbial activity toward an optimized outcome in efficient biodegradation processes conveys a promise of maintaining intensive yet sustainable agriculture. Efficient strategies for harnessing the native microbiome require the development of approaches for processing big genomic data. Here, we pursued metabolic modeling for promoting realistic solutions by simulation-based prioritization of alternative supplements as potential biostimulants, considering a collection of indigenous bacteria. Our genomic-based predictions point at strategies for optimizing biodegradation by the native community. Developing a systematic, data-guided understanding of metabolite-driven targeted enhancement of selected microorganisms lays the foundation for the design of ecologically sound methods for optimizing microbiome functioning.
农业化学物质的广泛使用正成为一个严重的环境问题，其代价是土壤和水资源受到污染。生物修复技术，如生物刺激，是一种有前途的策略，用于通过支持本土微生物降解剂来去除农业土壤中的污染物。虽然被认为具有成本效益和环保效益，但这些策略的成功率通常各不相同，因此很少被整合到商业农业实践中。在当前的研究中，我们应用基于代谢的群落模型方法，通过基于模拟的替代补充剂（如潜在生物刺激剂）的优先级排序来促进现实解决方案，同时考虑了一组本土细菌。通过模拟和盆栽实验验证，对生物刺激剂作为本土降解剂增强剂的功效进行了排名。通过二维模拟矩阵预测不同生物刺激剂对额外潜在本土降解剂（假单胞菌、、和）的影响，并与实验观察结果交叉。模型预测作为分类刺激剂的化合物的整体能力表明，计算算法可以指导土壤微生物组的操作，并为生物刺激策略的有针对性设计提供了另一个步骤。为不断增长的人口提供食物需要大量使用农药等农业化学物质。本土微生物土壤群落被认为是降解此类外源物质的关键参与者。在有效的生物降解过程中，操纵微生物活性以达到优化的结果，这为维持集约化但可持续的农业提供了希望。利用本土微生物组的有效策略需要开发处理大型基因组数据的方法。在这里，我们通过基于模拟的替代补充剂（如潜在生物刺激剂）的优先级排序来促进现实解决方案的代谢建模，同时考虑了一组本土细菌。我们基于基因组的预测指出了通过本土群落优化生物降解的策略。开发一种系统的、数据驱动的方法来了解代谢物驱动的选定微生物的靶向增强，为设计优化微生物组功能的生态合理方法奠定了基础。
