Energy Biosciences Institute, University of California, Berkeley , Berkeley, California 94720, United States.
Physical Biosciences Division, Lawrence Berkeley National Lab , Berkeley, California 94720, United States.
Environ Sci Technol. 2017 Jun 20;51(12):7278-7285. doi: 10.1021/acs.est.7b00686. Epub 2017 May 26.
The selective perturbation of complex microbial ecosystems to predictably influence outcomes in engineered and industrial environments remains a grand challenge for geomicrobiology. In some industrial ecosystems, such as oil reservoirs, sulfate reducing microorganisms (SRM) produce hydrogen sulfide which is toxic, explosive, and corrosive. Despite the economic cost of sulfidogenesis, there has been minimal exploration of the chemical space of possible inhibitory compounds, and very little work has quantitatively assessed the selectivity of putative souring treatments. We have developed a high-throughput screening strategy to identify potent and selective inhibitors of SRM, quantitatively ranked the selectivity and potency of hundreds of compounds and identified previously unrecognized SRM selective inhibitors and synergistic interactions between inhibitors. Zinc pyrithione is the most potent inhibitor of sulfidogenesis that we identified, and is several orders of magnitude more potent than commonly used industrial biocides. Both zinc and copper pyrithione are also moderately selective against SRM. The high-throughput (HT) approach we present can be readily adapted to target SRM in diverse environments and similar strategies could be used to quantify the potency and selectivity of inhibitors of a variety of microbial metabolisms. Our findings and approach are relevant to efforts to engineer environmental ecosystems and also to understand the role of natural gradients in shaping microbial niche space.
选择性地扰动复杂微生物生态系统,以可预测的方式影响工程和工业环境中的结果,这仍然是地质微生物学的一个重大挑战。在一些工业生态系统中,如油藏,硫酸盐还原微生物(SRM)会产生硫化氢,它具有毒性、爆炸性和腐蚀性。尽管硫化作用会带来经济成本,但对可能的抑制化合物的化学空间的探索很少,并且很少有工作定量评估潜在酸化处理的选择性。我们开发了一种高通量筛选策略来识别 SRM 的有效且选择性抑制剂,对数百种化合物的选择性和效力进行了定量排序,并确定了以前未被识别的 SRM 选择性抑制剂和抑制剂之间的协同作用。我们发现的吡啶硫酮锌是最有效的硫化抑制剂,其效力比常用的工业杀菌剂高几个数量级。锌和铜吡啶硫酮对 SRM 也具有中等选择性。我们提出的高通量(HT)方法可以很容易地适应针对不同环境中的 SRM,并且类似的策略可以用于量化各种微生物代谢抑制剂的效力和选择性。我们的发现和方法与工程环境生态系统的努力有关,也与理解自然梯度在塑造微生物生态位空间中的作用有关。