During the processing of refractory gold ores, cyanide (CN-) and residual sulphur species react to form an effluent stream containing thiocyanate (SCN-) and residual CN-. The release of SCN- and CN- containing effluent water to the environment is prohibited, necessitating effective treatment prior to discharge and/or reuse of contaminated plant water. Biologically mediated effluent remediation processes have been developed for commercial use, to remediate SCN-containing effluents, with the aim of enabling recycling of process water and improving the quality of effluent water prior to disposal. Bioremediation processes to treat these effluents rely on a complex consortium of microorganisms to metabolise the SCN- resulting in the production of ammonium that is in turn removed by conversion to nitrite and subsequent denitrification. Increasingly, genomic methods are being used to investigate processes in wastewater treatment to identify key microbial species and, thereby, inform the rationale design and operation of these bioremediation systems. The microbial ecology of laboratory-based SCN- degrading bioprocesses have been investigated, using genome resolved metagenomics, to provide detailed information on the community composition and metabolic profile of abundant microbial community members. Three Thiobacillus strains and an Afipia strain capable of SCN- degradation were shown to be highly abundant within a reactor system enabling biofilm formation. In contrast the inclusion of suspended mineral tailings within an alternative reactor system, led to the selection of an active planktonic microbial community with reduced microbial diversity containing only a single SCN-degrading Thiobacillus sp. This information is being used to inform further rational development of SCN- degradation processes for treatment of contaminated industrial wastewater effluents.

Thiocyanate (SCN^-) contamination threatens aquatic ecosystems and pollutes vital freshwater supplies. SCN^--degrading microbial consortia are commercially adapted for remediation, but the impact of organic amendments on selection within SCN^--degrading microbial communities has not been investigated. Here, we tested whether specific strains capable of degrading SCN^- could be reproducibly selected for based on SCN^- loading and the presence or absence of added organic carbon. Complex microbial communities derived from those used to treat SCN^--contaminated water were exposed to systematically increased input SCN concentrations in molasses-amended and -unamended reactors and in reactors switched to unamended conditions after establishing the active SCN^--degrading consortium. Five experiments were conducted over 790 days, and genome-resolved metagenomics was used to resolve community composition at the strain level. A single Thiobacillus strain proliferated in all reactors at high loadings. Despite the presence of many Rhizobiales strains, a single Afipia variant dominated the molasses-free reactor at moderately high loadings. This strain is predicted to break down SCN^- using a novel thiocyanate desulfurase, oxidize resulting reduced sulfur, degrade product cyanate to ammonia and CO₂ via cyanate hydratase, and fix CO₂ via the Calvin-Benson-Bassham cycle. Removal of molasses from input feed solutions reproducibly led to dominance of this strain. Although sustained by autotrophy, reactors without molasses did not stably degrade SCN^- at high loading rates, perhaps due to loss of biofilm-associated niche diversity. Overall, convergence in environmental conditions led to convergence in the strain composition, although reactor history also impacted the trajectory of community compositional change.

Metagenomic or metabarcoding data are often used to predict microbial interactions in complex communities, but these predictions are rarely explored experimentally. Here, we use an organism abundance correlation network to investigate factors that control community organization in mine tailings-derived laboratory microbial consortia grown under dozens of conditions. The network is overlaid with metagenomic information about functional capacities to generate testable hypotheses. We develop a metric to predict the importance of each node within its local network environments relative to correlated vitamin auxotrophs, and predict that a Variovorax species is a hub as an important source of thiamine. Quantification of thiamine during the growth of Variovorax in minimal media show high levels of thiamine production, up to 100 mg/L. A few of the correlated thiamine auxotrophs are predicted to produce pantothenate, which we show is required for growth of Variovorax, supporting that a subset of vitamin-dependent interactions are mutualistic. A Cryptococcus yeast produces the B-vitamin pantothenate, and co-culturing with Variovorax leads to a 90-130-fold fitness increase for both organisms. Our study demonstrates the predictive power of metagenome-informed, microbial consortia-based network analyses for identifying microbial interactions that underpin the structure and functioning of microbial communities.

Remediation of industrial wastewater is important for preventing environmental contamination and enabling water reuse. Biological treatment for one industrial contaminant, thiocyanate (SCN^-), relies upon microbial hydrolysis, but this process is sensitive to high loadings. To examine the activity and stability of a microbial community over increasing SCN^- loadings, we established and operated a continuous-flow bioreactor fed increasing loadings of SCN^-. A second reactor was fed ammonium sulfate to mimic breakdown products of SCN^-. Biomass was sampled from both reactors for metagenomics and metaproteomics, yielding a set of genomes for 144 bacteria and one rotifer that constituted the abundant community in both reactors. We analyzed the metabolic potential and temporal dynamics of these organisms across the increasing loadings. In the SCN^- reactor, Thiobacillus strains capable of SCN^- degradation were highly abundant, whereas the ammonium sulfate reactor contained nitrifiers and heterotrophs capable of nitrate reduction. Key organisms in the SCN^- reactor expressed proteins involved in SCN^- degradation, sulfur oxidation, carbon fixation, and nitrogen removal. Lower performance at higher loadings was linked to changes in microbial community composition. This work provides an example of how meta-omics can increase our understanding of industrial wastewater treatment and inform iterative process design and development.