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.

Gold ore processing uses cyanide (CN(-) ), which often results in large volumes of thiocyanate- (SCN(-) ) contaminated wastewater requiring treatment. Microbial communities can degrade SCN(-) and CN(-) , but little is known about their membership and metabolic potential. Microbial-based remediation strategies will benefit from an ecological understanding of organisms involved in the breakdown of SCN(-) and CN(-) into sulfur, carbon and nitrogen compounds. We performed metagenomic analysis of samples from two laboratory-scale bioreactors used to study SCN(-) and CN(-) degradation. Community analysis revealed the dominance of Thiobacillus spp., whose genomes harbour a previously unreported operon for SCN(-) degradation. Genome-based metabolic predictions suggest that a large portion of each bioreactor community is autotrophic, relying not on molasses in reactor feed but using energy gained from oxidation of sulfur compounds produced during SCN(-) degradation. Heterotrophs, including a bacterium from a previously uncharacterized phylum, compose a smaller portion of the reactor community. Predation by phage and eukaryotes is predicted to affect community dynamics. Genes for ammonium oxidation and denitrification were detected, indicating the potential for nitrogen removal, as required for complete remediation of wastewater. These findings suggest optimization strategies for reactor design, such as improved aerobic/anaerobic partitioning and elimination of organic carbon from reactor feed.

Thiocyanate (SCN^- ) is a toxic compound that forms when cyanide (CN^- ), used to recover gold, reacts with sulfur species. SCN^- -degrading microbial communities have been studied, using bioreactors fed synthetic wastewater. The inclusion of suspended solids in the form of mineral tailings, during the development of the acclimatized microbial consortium, led to the selection of an active planktonic microbial community. Preliminary analysis of the community composition revealed reduced microbial diversity relative to the laboratory-based reactors operated without suspended solids. Despite minor upsets during the acclimation period, the SCN^- degradation performance was largely unchanged under stable operating conditions. Here, we characterized the microbial community in the SCN^- degrading bioreactor that included solid particulate tailings and determined how it differed from the biofilm-based communities in solids-free reactor systems inoculated from the same source. Genome-based analysis revealed that the presence of solids decreased microbial diversity, selected for different strains, suppressed growth of thiobacilli inferred to be primarily responsible for SCN^- degradation, and promoted growth of Trupera, an organism not detected in the reactors without solids. In the solids reactor community, heterotrophy and aerobic respiration represent the dominant metabolisms. Many organisms have genes for denitrification and sulfur oxidation, but only one Thiobacillus sp. in the solids reactor has SCN^- degradation genes. The presence of the solids prevented floc and biofilm formation, leading to the observed reduced microbial diversity. Collectively the presence of the solids and lack of biofilm community may result in a process with reduced resilience to process perturbations, including fluctuations in the influent composition and pH. The results from this investigation have provided novel insights into the community composition of this industrially relevant community, giving potential for improved process control and operation through ongoing process monitoring.

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.