High-Throughput Screening To Identify Potent and Specific Inhibitors of Microbial Sulfate Reduction.

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.

Monofluorophosphate is a selective inhibitor of respiratory sulfate-reducing microorganisms.

Despite the environmental and economic cost of microbial sulfidogenesis in industrial operations, few compounds are known as selective inhibitors of respiratory sulfate reducing microorganisms (SRM), and no study has systematically and quantitatively evaluated the selectivity and potency of SRM inhibitors. Using general, high-throughput assays to quantitatively evaluate inhibitor potency and selectivity in a model sulfate-reducing microbial ecosystem as well as inhibitor specificity for the sulfate reduction pathway in a model SRM, we screened a panel of inorganic oxyanions. We identified several SRM selective inhibitors including selenate, selenite, tellurate, tellurite, nitrate, nitrite, perchlorate, chlorate, monofluorophosphate, vanadate, molydate, and tungstate. Monofluorophosphate (MFP) was not known previously as a selective SRM inhibitor, but has promising characteristics including low toxicity to eukaryotic organisms, high stability at circumneutral pH, utility as an abiotic corrosion inhibitor, and low cost. MFP remains a potent inhibitor of SRM growing by fermentation, and MFP is tolerated by nitrate and perchlorate reducing microorganisms. For SRM inhibition, MFP is synergistic with nitrite and chlorite, and could enhance the efficacy of nitrate or perchlorate treatments. Finally, MFP inhibition is multifaceted. Both inhibition of the central sulfate reduction pathway and release of cytoplasmic fluoride ion are implicated in the mechanism of MFP toxicity.

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop.

Computational tools addressing various components of design-build-test-learn (DBTL) loops for the construction of synthetic genetic networks exist but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called Design Assemble Round Trip (DART). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions. Graphical Abstract.