Biomining is a sustainable alternative to conventional mineral processing that uses acidophilic microorganisms to catalyze the extraction of valuable metals from sulfide minerals. Mixed microbial consortia composed of moderate thermophiles such as Sulfobacillus and some Leptospirillum species improve metal extraction efficiency at higher temperatures compared to pure cultures of mesophiles. However, quorum sensing (QS), which regulates microbial interactions and likely influences bioleaching performance, has not been studied in these species. In this study, treatment of a moderately thermophilic biomining consortium with QS compounds, termed diffusible signal factors (DSF), reduced pyrite and chalcopyrite dissolution via an inhibitory effect on iron oxidation and mineral colonization by the mixed culture. Furthermore, QS molecules changed the distribution of planktonic/mineral subpopulations of the acidophilic species. In addition, DSF compounds induced Acidithiobacillus caldus motility and dispersion from pyrite with a concomitant expansion of Leptospirillum ferriphilum on the mineral surface while in contrast, the acyl-homoserine lactone mediated QS system repressed L. ferriphilum motility. Moreover, the addition of QS molecules induced a second response related to the detrimental effect of high concentrations of fatty acids on cells, with an activation of detoxification mechanisms. Overall, QS regulated key target microbial interactions that opens the possibility to improve chalcopyrite bioleaching in the studied consortia.

Biomining is the extraction of target metals from ores or wastes such as the dissolution of chalcopyrite for copper recovery. A key outstanding topic of study is to improve the rate and total copper released from chalcopyrite that can become 'passivated' by surface layers, which hinders oxidative attack on the metal sulfide bond. One strategy to increase chalcopyrite bioleaching is to control of the oxidation-reduction potential in the desired range by using 'weak' iron oxidizers. In this study, 15 acidophilic species were evaluated for their ability to catalyze chalcopyrite dissolution that resulted in the addition of Acidithiobacillus ferrianus, Sulfobacillus thermotolerans, and Metallosphaera sedula to the known 'weak' iron oxidizing species. Based upon these data, four microbial consortia were designed including mesophiles (25 degrees and 37 degrees C), moderate thermophiles (49 degrees C), and thermophiles (70 degrees C) that increased copper recoveries by up to 32% compared to abiotic controls. The best performing consortium was the moderate thermophiles Sulfobacillus thermotolerans, Sulfobacillus acidophilus, and Ferroplasma acidiphilum that maintained the oxidation-reduction potential in the desired range. However, the consortia also showed evidence of synergistic interactions between 'weak' iron oxidizers that increased the efficiency of ferrous iron oxidation that resulted in oxidation-reduction potentials above the desired range and lower copper release. Therefore, while designing microbial consortia is a promising strategy to improve the performance of chalcopyrite bio-leaching, care must be taken to ensure synergistic effects do not result in high oxidation-reduction potentials.

Bioleaching of metal sulfide ores involves acidophilic microbes that catalyze the chemical dissolution of the metal sulfide bond that is enhanced by attached and planktonic cell mediated oxidation of iron(II)-ions and inorganic sulfur compounds. Leptospirillum spp. often predominate in sulfide mineral-containing environments, including bioheaps for copper recovery from chalcopyrite, as they are effective primary mineral colonizers and oxidize iron(II)-ions efficiently. In this study, we demonstrated a functional diffusible signal factor interspecies quorum sensing signaling mechanism in Leptospirillum ferriphilum and Leptospirillum ferrooxidans that produces (Z)-11-methyl-2-dodecenoic acid when grown with pyrite as energy source. In addition, pure diffusible signal factor and extracts from supernatants of pyrite grown Leptospirillum spp. inhibited biological iron oxidation in various species, and that pyrite grown Leptospirillum cells were less affected than iron grown cells to self inhibition. Finally, transcriptional analyses for the inhibition of iron-grown L. ferriphilum cells due to diffusible signal factor was compared with the response to exposure of cells to N- acyl-homoserine-lactone type quorum sensing signal compounds. The data suggested that Leptospirillum spp. diffusible signal factor production is a strategy for niche protection and defense against other microbes and it is proposed that this may be exploited to inhibit unwanted acidophile species.

Vanadium - a transition metal - is found in the ferrous-ferric mineral, magnetite. Vanadium has many industrial applications, such as in the production of high-strength low-alloy steels, and its increasing global industrial consumption requires new primary sources. Bioleaching is a biotechnological process for microbially catalyzed dissolution of minerals and wastes for metal recovery such as biogenic organic acid dissolution of bauxite residues. In this study, 16S rRNA gene amplicon sequencing was used to identify microorganisms in Nordic mining environments influenced by vanadium containing sources. These data identified gene sequences that aligned to the Gluconobacter genus that produce gluconic acid. Several strategies for magnetite dissolution were tested including oxidative and reductive bioleaching by acidophilic microbes along with dissimilatory reduction by Shewanella spp. that did not yield significant metal release. In addition, abiotic dissolution of the magnetite was tested with gluconic and oxalic acids, and yielded 3.99 and 81.31% iron release as a proxy for vanadium release, respectively. As a proof of principle, leaching via gluconic acid production by Gluconobacter oxydans resulted in a maximum yield of 9.8% of the available iron and 3.3% of the vanadium. Addition of an increased concentration of glucose as electron donor for gluconic acid production alone, or in combination with calcium carbonate to buffer the pH, increased the rate of iron dissolution and final vanadium recoveries. These data suggest a strategy of biogenic organic acid mediated vanadium recovery from magnetite and point the way to testing additional microbial species to optimize the recovery.

Background: Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. This approach allows to identify signalling pathways involved in specific biological functions. The ability to infer causality in gene regulatory networks, in addition to correlation, is crucial for several modelling approaches and allows targeted control in biotechnology applications. Methods: We performed simulations according to the approximate Bayesian computation method, where the core model consisted of a steady-state simulation algorithm used to study gene regulatory networks in systems for which a limited level of details is available. The simulations outcome was compared to experimentally measured transcriptomics and proteomics data through approximate Bayesian computation. Results: The structure of small gene regulatory networks responsible for the regulation of biological functions involved in biomining were inferred from multi OMICs data of mixed bacterial cultures. Several causal inter- and intraspecies interactions were inferred between genes coding for proteins involved in the biomining process, such as heavy metal transport, DNA damage, replication and repair, and membrane biogenesis. The method also provided indications for the role of several uncharacterized proteins by the inferred connection in their network context. Conclusions: The combination of fast algorithms with high-performance computing allowed the simulation of a multitude of gene regulatory networks and their comparison to experimentally measured OMICs data through approximate Bayesian computation, enabling the probabilistic inference of causality in gene regulatory networks of a multispecies bacterial system involved in biomining without need of single-cell or multiple perturbation experiments. This information can be used to influence biological functions and control specific processes in biotechnology applications.

Society's demand for metals is ever increasing while stocks of high-grade minerals are being depleted. Biomining, for example of chalcopyrite for copper recovery, is a more sustainable biotechnological process that exploits the capacity of acidophilic microbes to catalyze solid metal sulfide dissolution to soluble metal sulfates. A key early stage in biomining is cell attachment and biofilm formation on the mineral surface that results in elevated mineral oxidation rates. Industrial biomining of chalcopyrite is typically carried out in large scale heaps that suffer from the downsides of slow and poor metal recoveries. In an effort to mitigate these drawbacks, this study investigated planktonic and biofilm cells of acidophilic (optimal growth pH < 3) biomining bacteria. RNA and proteins were extracted, and high throughput "omics" performed from a total of 80 biomining experiments. In addition, micrographs of biofilm formation on the chalcopyrite mineral surface over time were generated from eight separate experiments. The dataset generated in this project will be of great use to microbiologists, biotechnologists, and industrial researchers.

Background: Deep neural networks have been successfully applied to diverse fields of computer vision. However, they only outperform human capacities in a few cases. Methods: The ability of deep neural networks versus human experts to classify microscopy images was tested on biofilm colonization patterns formed on sulfide minerals composed of up to three different bioleaching bacterial species attached to chalcopyrite sample particles. Results: A low number of microscopy images per category (<600) was sufficient for highly efficient computational analysis of the biofilm's bacterial composition. The use of deep neural networks reached an accuracy of classification of ∼90% compared to ∼50% for human experts. Conclusions: Deep neural networks outperform human experts’ capacity in characterizing bacterial biofilm composition involved in the degradation of chalcopyrite. This approach provides an alternative to standard, time-consuming biochemical methods. © 2019 The Author

Industrial biomining processes are currently focused on metal sulfides and their dissolution, which is catalyzed by acidophilic iron(II)- and/or sulfur-oxidizing microorganisms. Cell attachment on metal sulfides is important for this process. Biofilm formation is necessary for seeding and persistence of the active microbial community in industrial biomining heaps and tank reactors, and it enhances metal release. In this study, we used a method for direct quantification of the mineral-attached cell population on pyrite or chalcopyrite particles in bioleaching experiments by coupling high-throughput, automated epifluorescence microscopy imaging of mineral particles with algorithms for image analysis and cell quantification, thus avoiding human bias in cell counting. The method was validated by quantifying cell attachment on pyrite and chalcopyrite surfaces with axenic cultures of Acidithiobacillus caldus, Leptospirillum ferriphilum, and Sulfobacillus thermosulfidooxidans. The method confirmed the high affinity of L. ferriphilum cells to colonize pyrite and chalcopyrite surfaces and indicated that biofilm dispersal occurs in mature pyrite batch cultures of this species. Deep neural networks were also applied to analyze biofilms of different microbial consortia. Recent analysis of the L. ferriphilum genome revealed the presence of a diffusible soluble factor (DSF) family quorum sensing system. The respective signal compounds are known as biofilm dispersal agents. Biofilm dispersal was confirmed to occur in batch cultures of L. ferriphilum and S. thermosulfidooxidans upon the addition of DSF family signal compounds. IMPORTANCE The presented method for the assessment of mineral colonization allows accurate relative comparisons of the microbial colonization of metal sulfide concentrate particles in a time-resolved manner. Quantitative assessment of the mineral colonization development is important for the compilation of improved mathematical models for metal sulfide dissolution. In addition, deep-learning algorithms proved that axenic or mixed cultures of the three species exhibited characteristic biofilm patterns and predicted the biofilm species composition. The method may be extended to the assessment of microbial colonization on other solid particles and may serve in the optimization of bioleaching processes in laboratory scale experiments with industrially relevant metal sulfide concentrates. Furthermore, the method was used to demonstrate that DSF quorum sensing signals directly influence colonization and dissolution of metal sulfides by mineral-oxidizing bacteria, such as L. ferriphilum and S. thermosulfidooxidans.

Leptospirillum ferriphilum plays a major role in acidic, metal rich environments where it represents one of the most prevalent iron oxidizers. These milieus include acid rock and mine drainage as well as biomining operations. Despite its perceived importance, no complete genome sequence of this model species' type strain is available, limiting the possibilities to investigate the strategies and adaptations Leptospirillum ferriphilum^T applies to survive and compete in its niche. This study presents a complete, circular genome of Leptospirillum ferriphilum^T DSM 14647 obtained by PacBio SMRT long read sequencing for use as a high quality reference. Analysis of the functionally annotated genome, mRNA transcripts, and protein concentrations revealed a previously undiscovered nitrogenase cluster for atmospheric nitrogen fixation and elucidated metabolic systems taking part in energy conservation, carbon fixation, pH homeostasis, heavy metal tolerance, oxidative stress response, chemotaxis and motility, quorum sensing, and biofilm formation. Additionally, mRNA transcript counts and protein concentrations were compared between cells grown in continuous culture using ferrous iron as substrate and bioleaching cultures containing chalcopyrite (CuFeS₂). Leptospirillum ferriphilum^T adaptations to growth on chalcopyrite included a possibly enhanced production of reducing power, reduced carbon dioxide fixation, as well as elevated RNA transcripts and proteins involved in heavy metal resistance, with special emphasis on copper efflux systems. Finally, expression and translation of genes responsible for chemotaxis and motility were enhanced.